Human Factors and Ergonomics in Industry 5.0—A Systematic Literature Review

Abstract

Featured Application

This paper supports the design and implementation of human-centric work environments in industrial and organizational settings. By addressing traditional and cognitive ergonomics, workload management, and employee well-being, the findings can be applied to develop socio-technical systems that enhance productivity, efficiency, and motivation. Industries can leverage these insights to create safer and more sustainable workplaces, aligning with Industry 5.0 principles while improving overall market competitiveness.

Abstract

Human-centricity, sustainability, and resilience are the core pillars of the Industry 5.0 concept. The human-centric perspective emphasizes the development of socio-technical systems designed to enhance human health, safety, and well-being while fostering sustainable practices that benefit society at large. This paper presents a systematic literature review to identify the key characteristics of human-centered work environments. The findings reveal growing interest in human factors and ergonomics, with notable gaps in cognitive ergonomics requiring further attention. Beyond ensuring safety and health, human-centric systems must address cognitive workload and well-being to maintain productivity, efficiency, and motivation, which are closely tied to a company’s market performance. This study provides valuable insights for both scientific and industrial stakeholders, outlining the principles and requirements essential for the effective implementation of human-centric systems.

1. Introduction

Industry 5.0 represents a human-centered, resilient, and sustainable system that should overcome the barriers of the previously presented Industry 4.0 concept, which has not been implemented in the manufacturing industry on the expected level mostly due to its very challenging implementation process in which the focus was set on automatization and digitalization that only referred to technology while neglecting the human role and perspective. This is why Industry 5.0 was presented as a socio-technical (manufacturing) system in which the human is back at the center, and the human’s relations and communication with machines are now one of the most common challenges. Industry 4.0 centers on the digital transformation of industries, leveraging smart technologies to automate and optimize processes. In contrast, Industry 5.0 is about combining human intelligence and creativity with advanced technologies to create a more sustainable, human-centric, and ethical industrial environment [1]. Owing to numerous unforeseen global events and volatile market conditions, Industry 5.0 requires not only resilience but also sustainability with an impact on wider society [2]. Humans remain the primary drivers of the production system, necessitating support from those responsible for managing production systems. Therefore, it is imperative to address the adaptation of technology to human needs [3].

With technology replacing less qualified human jobs, there is a shift toward more complex human roles and an increased emphasis on interdisciplinary cooperation. Enterprises should now prioritize ergonomics for enhanced productivity while preventing workplace-related illnesses and injuries. Providing operators with not only physical but also cognitive support tools is crucial for ensuring efficient work [4]. Integrating collaborative machines can reduce ergonomic and cognitive loads on human workers, ensuring safety, proper monitoring, and increased productivity by speeding up production processes, reducing errors, and improving the quality of the resulting products, which will lead to greater competitiveness of the company in the market. On the other hand, most SMEs lack in-house knowledge and skills for the implementation of such collaborative technologies [5]. Furthermore, these companies may be confronted with high upfront costs, the need to upgrade infrastructure, and possible employee resistance to change. However, to overcome these barriers, SMEs can partner with technology companies that can provide the necessary support and training or invest in skills development for their employees. This collaboration can be made even more effective by applying a human-centered design approach that focuses on incorporating human factors into production systems and addresses their physical, psychological, social, and cultural needs [6].

The motivation for this paper was to recognize the main characteristics of human-centric systems and their interrelation with human factors and ergonomics. Therefore, the systematic literature review has been conducted to answer the following research questions:

• RQ1:. What are the main characteristics of a human-centered work environment?

• RQ2:. What are the dimensions the manufacturing company has to fulfill to create a human-centered work environment?

• RQ3:. Which principles and elements of human factors and ergonomics support (enable) or hinder (act as barriers to) creating a human-centered work environment?

2. Materials and Methods

To gain insight into the most relevant work published in the literature, the Web of Science (WoS) database was browsed, and the results were evaluated by principles of the PRISMA method [7]. Web of Science is known for its rigorous selection process. It focuses on including high-quality, peer-reviewed journals and excludes predatory journals more effectively than other scientific databases. WoS prioritizes indexing only the most impactful journals, ensuring that the literature cited comes from sources that have undergone strict editorial oversight. It includes long-standing indexes such as the Science Citation Index (SCI) and the Social Sciences Citation Index (SSCI), which have been the gold standard for decades in evaluating scientific research. This ensures that the sources retrieved are foundational, often-cited works in their respective fields, providing a strong basis for a literature review. WoS databases are often better curated for STEM fields, including engineering, as well as for social sciences. The depth and specificity of indexing make it easier to find seminal works in these areas. Others, such as Scopus for example, while broader, may include journals and conference papers that are less relevant or peripheral to high-impact research. While the above advantages support using WoS, it is also important to note that other databases such as Scopus are broader and include journals not covered by WoS, which could offer additional perspectives. However, for a high-impact and scientifically rigorous literature review, Web of Science remains the more trusted source. Relying on Web of Science, we ensured that the literature review is based on high-quality, rigorously vetted, and impactful sources, enhancing the credibility and academic value of the scientific paper.

The objective was to understand the characteristics of human-centricity within Industry 5.0 and explore the correlation between Industry 5.0, human-centricity, and human factors and ergonomics. This is why the database was searched using the following strings (Figure 1):

• “Ergonomics 4.0” (379 records);

• “Operator 5.0” (109 records);

• “Human-centered AND Industry 5.0” (107 records);

• “Human-centered AND Industry 5.0 OR Manufacturing OR Production” (128 records);

• “Human-centred AND ergonomics” (260 records).

Each searched term was refined by the field of engineering (industrial, manufacturing, multidisciplinary). Papers available until October 2024 were included in this research. The inclusion criteria for this systematic literature review on human factors and ergonomics in Industry 5.0 were established based on predefined eligibility parameters related to study relevance and scope, which are predominantly related to the industrial engineering field. This would include the aspects of both the manufacturing industry but also human resources management.

One reviewer browsed the database by the keywords and extracted the most relevant data (title, authors, abstract, keywords, year published) to an Excel sheet. Two reviewers then independently screened each record and full-text report retrieved from the databases to ensure unbiased selection. The screening process was performed in two stages: title and abstract screening, followed by full-text review. Any discrepancies between the reviewers were resolved through discussion or consultation with a third reviewer. MS Excel was also used to identify duplicates. If studies reported multiple measures within an outcome domain, all compatible results were considered. However, in cases where studies provided conflicting or redundant results, the decision to include specific results was based on the methodological quality, relevance, and clarity of reported data. This is how the efficiency and consistency was ensured in handling a large volume of records. The combined manual and semi-automated approach ensured a thorough and systematic selection of studies.

Among the 983 records found, 119 were chosen for the detailed analysis (Figure 1). The full papers that were not available to the authors were not included in the research, but their abstracts with the brief results were included, which can be recognized as a limitation of this research. In total, 12 such papers have been identified. Because the number of those papers is relatively low compared with the total number of full papers screened, this is considered to not have a significant impact on the final results.

3. Results

The majority of the papers were published in 2021–2024. The first paper considered in the review was published in 2014, and since then, the growth of interest for the topic is noticed (Figure 2).

To understand the principles of human-centered manufacturing and its correlation with Ergonomics 5.0, after full paper screening, the authors have divided the records into the following groups based on the thematic scope of each paper:

• (1). Operator 4.0/5.0;

• (2). UX design and leadership;

• (3). Digital human modeling;

• (4). Wearables and hardware;

• (5). Aging population;

• (6). Collaborative robotics and human–machine relations;

• (7). Human factors and ergonomics (general);

• (8). Occupational health and safety;

• (9). Well-being.

3.1. From Operator 4.0 to 5.0

With the continuing evolution of technology and the increasing need to adapt industrial processes to human values and sustainability, the role of the operator in the manufacturing environment is constantly evolving. The operator is no longer just a passive user of technology but becomes an active partner in the interaction with advanced autonomous systems in the production process. Operator 4.0 is a human worker in the era and the work conditions according to Industry 4.0 principles, defined as “an industrial worker whose cognitive, sensorial, physical and interaction capabilities are enhanced by the close interplay with Industry 4.0 technologies”. The value-sensitive design (VSD) methodology is proposed as one way that Operator 4.0 technologies can be designed for these human values [8]. Technologies enhancing the operator’s cognitive capabilities are cloud computers, simulation, virtual reality, and artificial intelligence; those enhancing the operator’s sensorial capabilities are health monitoring sensors, personal activity trackers, posture sensors, and IoT; those enhancing the operator’s physical capabilities are collaborative robots, exoskeletons, actuators, and control devices, and teleoperated systems; and those enhancing interaction capabilities are human–machine interfaces, augmented reality, mobile devices, and personal intelligence assistance [9]. Because there is a distinction between machine-readable knowledge and tacit knowledge, it is suggested that rather than seeking to automate a human activity, workplace automation should be developed so that human intelligence and knowledge-based activity are placed at the center of a systems development effort, which is how humans can take advantage of machines [10]. With that said, additional approaches define the characteristics of Operator 4.0 as augmented and virtual operators (extended reality (XR) technologies), healthy operators (smart wearable solutions related to health metrics), smarter operators (intelligent personal assistant and softbots), social operators (social networking services in real-time), and analytical operators (big data analytics to prevent mistakes and decision support) [8].

The novel concept raises demand for Operator 5.0, a reconsideration of Operator 4.0 by the human-centric principles on which human factors and ergonomics have a direct influence. Along with the flexible architecture that enables the integration from different data sources in terms of the Industry 5.0 human-centric system, the human dimension must be incorporated to increase business value creation. Human-centered processes are typically designed to address a specific business issue, as most of these workflows are related to team collaboration. They see the value in the fusion of wearable devices connected to human biosensing dimensions, so they enhance the need for the standardization of integrated business processes that involve both automated and human-driven operations [1]. Similarly to Operator 4.0, Operator 5.0 should collaborate with the equipment according to their physical, sensorial, and cognitive capabilities to provide a safe environment and optimal utilization of equipment. This approach not only takes into account the operator’s individual capabilities but also integrates modern technologies to provide real-time information. These technologies allow the operator to make accurate decisions and adapt work processes to the current conditions and needs, thereby increasing overall efficiency and safety. Research in the field of technologies adapted to human needs focuses on the development of networked sensors with low-level intelligence (to reduce network overload and allow for the exchange of important data), the creation of digital twins (for production monitoring and prediction), virtual training (to avoid a dangerous situation in training and education), and artificial intelligence (for machines and robots to learn from humans) [8]. To ensure the long-term added value of these technologies in the work environment, it is crucial that they provide a positive user experience, which needs to be systematically analyzed and optimized. However, the implementation of these technologies in the workplace can have both positive and negative impacts on their operators. These technologies can also act as a sophisticated monitoring system to ensure optimal worker health [9]. Although new intelligent systems will improve access to data, their massive generation can lead to information overload or misuse. The potential reduction in the workload on the operator through new technologies also raises questions about the economic use of the time saved and the risk of replacing it with other more burdensome tasks [10]. To avoid these negative impacts, an Operator 5.0 should also be creative, problem-solving, and use their intuition and experience to identify potential areas for improvement in production processes and product quality [11]. The results show that the transition to Operator 5.0 is inevitable, but new technologies have not yet been sufficiently explored and are not ready for effective integration of the human factor. There is a lack of research focusing on integrated sustainability from a human perspective and on system resilience, particularly with regard to the key drivers and constraints affecting the implementation of these technologies [12]. This concept goes beyond traditional industrial needs and focuses on sustainability and human well-being, creating a creative Society 5.0 where digital transformation is combined with the imagination and creativity of individuals [13]. The benefits of Shop Floor 5.0 are focus on delivering customer experience, hyper-customized, responsive, distributed value chain, and experience-activated (interactive) products and the return of manpower to the factories [14]. The summary of the key findings of the research results related to Operator 4.0 and 5.0 can be found in Table 1.

3.2. UX Design and Leadership

The development of new technologies and the Industry 5.0 concept require a new management strategy and perspective. This approach may include a combination of leadership and UX design (user experience design). While leadership focuses on managing and motivating employees effectively, UX design focuses on creating optimal user experiences. The combination of these two approaches can help to better integrate employee needs and user experience, positively impacting overall business performance [15].

The workplace design phase is crucial in providing a sustainable and resilient workplace by human factors and ergonomics standards. It is important to involve employees from the earliest stages of a design project to ensure their health and safety in work conditions [16]. Designers have to consider several criteria such as the variability of the workers’ population and anthropometry. It is suggested that workers wear devices to collect data, and the analysis is provided by a simulation [17]. The integrated employee’s self-reported data and objective event-based data from in-the-field video-analysis leads to an adequate understanding of the work activities and related main issues while finally providing more effective digital tools to employees for their everyday work support. Such an employee-centered approach can ease the introduction of portable and wearable devices but also deal with its potential privacy-related issues [18]. A human-centered approach combined with lean tools such as visual management, error prevention, and waste analysis can reduce human errors [19]. Many current planning models developed for managerial decision support do not consider human factors and their impact on system or employee performance, which leads to inaccurate planning results but also increased health hazards for employees [5]. The strategic goal is to buy into an agile workforce that would continuously develop their competencies [20].

Individual solutions ensure diversity in employees, including their perceptual, cognitive, and physical capabilities and needs. The workplace design should also be age-friendly, where technologies are enhancing human capacity, not substituting them, which is one of the characteristics of human-centricity. This includes the implementation of assistive and collaborative technologies. The understanding of the correct balance between automating manual activities and those tasks remaining for the human worker is of very high importance, but the attention must also be set on the secondary effects of automation and assistive or collaborative technologies, as there is a risk of overloading workers with monotonous activities. If not considered, there is an occurrence of “phantom profits”, which refers to the “anticipated profits of an investment that fail to appear as they are eroded by HF problems and resulting underperformance of the system”. Therefore, advanced technologies lead to new management strategies that are more decentralized, more autonomous, more intelligent, and data-driven [21]. In production line assembly, the main criteria are optimization of investment costs and ergonomics with fatigue and recovery criteria. Fatigue and recovery can be linearized with mixed integer linear programming formulation as a multi-objective solving algorithm [22].

There is a difference between human-centered design and user-centered design, which is shown by measuring the maturity of human-centeredness of product planning and control (PPC) systems. The results show that there is a direct link between cognitive biases and the design of PPC systems; however, a standardized guideline for the design of such systems is necessary [3]. This is related to dealing with the complexity of human cognition and decision-making, the impact of demographic change, communication aspects, interdisciplinarity, technology acceptance, dealing with user experience, respect for human emotions, and providing empirical metrics [23]. The human-centered design implies both achieving the worker well-being and high system performance by using new technologies. The unique combination of HF/E aspects (i.e., focus on human well-being and system performance), strategy design (i.e., BSC, objectives, and targets), and work systems aspects (SOFT dimensions) and the seven-step approach for application presents a novel method for designing work systems [6]. Current tools consider productivity and worker well-being aspects, but they fail to account for the anthropometric diversity. In this context, it is possible to optimize the workplace through a multi-objective optimization of the average RULA score [24]. Introducing gamification elements that promote employee engagement and motivation to meet ergonomic standards and improve work practices can also be an effective strategy to achieve these goals. With the majority of businesses now predominantly using mobile applications and web-based platforms, with computers, tablets, and mobile phones being the most common devices, it is important that technology effectively responds to current trends and needs [25]. Interfaces should have a training model for the unskilled workers based on their capabilities and actual understanding of the work [26]. Information provided to the user in collaborative systems is essential. Notifications and prompts should be intuitive and unobtrusive over long periods. They should be designed considering individual user experience and security standards [27]. The user interface is set to collect user feedback for data regarding effectiveness, efficiency, and satisfaction [28]. The mixed reality-based worker interface for industrial camera 3D positioning is intuitive and user-friendly to enhance safety, productivity, and ergonomics [29]. In this context, the MoCap (motion capture) system provides an important technology for analyzing and optimizing worker movements. This system enables accurate real-time motion capture and analysis, which contributes to a better understanding of ergonomic problems and the adaptation of the working environment. Thus, integrating MoCap into work systems can significantly support the personalization of training and improve ergonomics, contributing to a more efficient and safer work environment [30]. The integration of the MoCap system with AI could significantly improve the analysis, customization, and optimization of working conditions and processes. However, increased application and use of AI have an impact on socio-technical work systems, so certain challenges in leadership occur. Therefore, for all relevant stakeholders to be involved in the change process, leaders must decide which activities to be taken over by humans and which by AI. AI also raises certain ethical concerns, in this case (1) self-determination, (2) justice, and (3) protection, privacy, and personality. Four clusters with individual criteria are recommended: (1) protection of the individual, (2) trustworthiness, (3) useful work sharing, and (4) conducive working conditions, which include the aspects of communication, cooperation, and social inclusion [31].

Ethical issues are one of the important challenges in human-centered 5.0 systems. The issues are mostly with respect to privacy, personal ethical issues, cultural ethics, and bias/stereotyping. Therefore, an open communication policy is essential when interacting with the user’s personal information to answer questions regarding how long their data will be stored and used and to treat user data with special care and respect [32]. The recommendations focus on human resource managers and encourage them to create training programs that are consistent with the company policy. In addition, it is essential that organizations regularly review their ethical standards and adapt them to new technological and societal challenges. This includes training employees in ethics, establishing ethics committees, and regularly assessing the impact of technology on different user groups [33]. Joint cognitive systems (a concept with all relevant stakeholders including ethical aspects in the design and development considered) propose complementary approaches and methods such as actor–network theory, the concept of operations, and ethically aware design. Cybertechnologies have increased the cognitive capabilities of machines significantly, which allows for the collaborative work of humans and machines as a team, and their behavior can be predicted. Actor–Network theory provides methods to analyze and observe a gradually evolving, dynamic team of various actors, thus understanding how the roles of the actors and the work allocation evolve. While traditional design methods tend to see humans as users or operators, actor–network theory sees human and machine actors equally and thus can support analyzing actual collaborations to identify where it works well and where it could be improved [34]. This approach is closely related to the concept of sustainable work. Sustainable work is based on the concepts of activity-centered ergonomics (ACE) and psychodynamics of work (PDW). Improvement actions are usually mitigatory or compensatory, acting on the effects while the root causes remain untouched. Sustainability initiatives focus on individual issues, in most cases on the leader (individual), narrowing its scope and neglecting broader, important topics such as work organization and work content. Both the work overload and work for sustainability agenda are usually disregarded. A comprehensive view of health should not be limited to the workplace. Sustainable work includes but is not limited to the integration of corporate sustainability guidelines and respecting human rights and labor laws, but it is also aimed at creating meaningful and pleasurable work, which leads to happiness and recognition. Sustainable work includes the caution of workers health and well-being while fostering creativity, which should be incorporated in the organizational culture [35]. The summary of the key findings of the research results related to UX design and leadership can be found in Table 2.

3.3. Digital Human Modeling

Digital human modeling is a technological process that involves creating digital representations of the human body and its functions and interactions with different systems or environments. This approach is used in several fields, including ergonomics, biomechanics, healthcare, and design, and it aims to simulate and analyze human behavior, movements, and interactions in digital environments [36].

Virtual ergonomics allow for the preliminary verification of posture-related issues while increasing well-being. The definition of new design experiences and multiple scenarios in the digital era plays a central role in manufacturing to focus on the safety of workers [37]. A predictive model could be useful for the evaluation of the biomechanical overload risk. The ergonomic validation procedure based on simulation is a synergy between the digital human model for ergonomic index assessment by the standard methods such as OWAS, NIOSH, OCRA, EAWS, and others [38]. One of these tools is JACK software, an advanced ergonomic simulation tool that enables a detailed analysis of work scenarios and workspace design using 3D modeling. Its features include simulation of human movements, ergonomic risk analysis, and optimization of working conditions, which contribute to improving the safety and efficiency of the working environment [39]. Another ergonomic software is HumanCAD, which is designed to create and analyze digital models of the human body in different working environments. It allows for the design and optimization of workspaces, taking into account anthropometric data and movement characteristics of users. Users can record movement positions via Microsoft Kinect devices. In addition, HumanCAD offers integrated features for applying various biomechanical models and performing complex ergonomic analyses, including methods such as NIOSH (National Institute for Occupational Safety and Health), RULA (Rapid Upper Limb Assessment) or OWAS (the Ovako Working Posture Assessment) [40]. The other simulation software used in one approach is Siemens Tecnomatix, where the OWAS method is implementable. The data can be captured via immersive reality tools and special motion capture systems such as cyber gloves [17]. A virtual reality-based multiplayer tool exploits low-cost body tracking technology to evaluate ergonomic postural risk. This allows for real-time evaluation but also an offline ergonomic risk assessment by RULA principles. This is how ergonomic experts have an immersive visualization of postures, even in offline mode, which allows for a sustainable approach to user-centered collaborative design. Data are captured by sensors located on the workers’ bodies (such as accelerometers, gyroscopes, magnetometers, electrocardiograms, and electromyographs). The authors achieved data collection through the use of Microsoft Kinect, a portable device that does not require optical markers for body tracking. The challenges they see in using this technology are: the influence of ambient lighting conditions, the lack of wrist joint tracking, and self-occlusions in postures such as arm crossing, trunk flexion lateral trunk flexion, and trunk rotation [41]. This kind of approach can also be used for the design of active exoskeletons for arms and shoulders [42]. It is important to link the external body geometry and internal skeleton correctly to improve the realism of digital human modeling and adequate biomechanical analysis, which requires data on joint load and muscle force estimation [43]. The summary of the key findings of the research results related to digital human modeling can be found in Table 3.

3.4. Wearables and Hardware

Over the past decade, the integration of wearable devices for ergonomic analysis in manufacturing has become a topic of considerable interest. This trend reflects the growing need for more accurate and efficient workload assessment in the modern work environment [44]. The choice of a specific wearable device is always conditioned by the measurement objectives, user characteristics, and available resources. It is also necessary to take into account the technological capabilities of these devices, including measurement accuracy and the ability to integrate with other systems [45]. In the novel work environment, the simultaneous analysis of physical and cognitive workload should be provided by non-invasive wearable devices that monitor human activity and physiological parameters, combined with questionnaires for subjective self-assessment [46]. Those parameters are: human body segment position and motions into the three-dimensional space, collected by motion capture systems; pupil diameter collected by an eye tracking system; physiological parameters referring to the user’s cardiovascular activity (i.e., heart rate and respiratory rate) and skin conductance, collected by biometric wearable devices; subjective assessment based on NASA-TLX (Task Load Index) questionnaires; and performance data like execution time, collected by video analysis. Another possibility is the use of digital assistants, software tools and devices that provide intuitive and rapid access to information and knowledge processed and made available by ubiquitous artificial intelligence, but they raise some ethical concerns. For example, Apple’s Siri is built into most Apple devices and allows users to control phone functions with voice commands. Google Assistant provides similar services but is available on a wide range of devices and integrates with various apps and services to manage tasks and provide information. Amazon Alexa is another example of a digital assistant used in smart home devices to control home electronics and provide information on demand [47]. A value-sensitive design (VSD) approach illustrates how technologies enabling human–machine symbiosis in the smart factory can be designed to embody elicited human values [48]. Internet of Things, combined with specialized software, enables self-learning in real time and tracking of the work results. This can improve workers’ mobility and support the implementation of new technologies, but at the same time, it raises some safety data concerns. Most common in the literature are large displays, touch and touchless interaction, and virtual reality (VR) [49].

There are various data-collecting possibilities. One of the topics currently being researched is the provision of stable body positions over extended periods of time. In this area, smart soles have been developed that serve as wearable devices for collecting data on postural characteristics [50]. Posture monitoring sensors such as Lumo Lift and Upright Go are designed to monitor and provide real-time feedback on posture quality. These devices help users maintain proper ergonomic postures, contributing to the prevention of back pain and improved overall health [39]. In addition, an innovative method that uses gyroscopes to measure the angular velocities of upper limb movement in all planes has been proposed to minimize the risk of musculoskeletal disorders associated with high movement speeds [51]. Furthermore, an AI-based system has been developed that uses 2D LiDAR and smartwatches to track a worker’s position according to ISO 11226 [52] during standardized tasks. The research involved collecting data from 30 participants during six typical assembly tasks, and the results showed that the system achieved an average position tracking accuracy of almost 98% [53]. There is also the monitoring system wherein eye tracking can be equipped with a wearable biosensor. Virtual prototypes conveniently simulate human–machine interactions to avoid bottlenecks and provide optimized workflows. The monitoring of human physiological responses enables the objectification of user experience, UX—heart rate, heart rate variability (a physical and mental stress condition), breathing rate (for physical stress and fatigue detection), pupil dilatation, eye gaze, eye blinks (operators’ visual interaction with devices, interfaces, machines, or people), and postular acceleration (for body monitoring movements) [54].

Studies have shown that among the tracking and assistance gadgets (smartphones, tablets, smartwatches, and data glasses), workers prefer smartwatches and smartphones. A smartwatch is most preferred, but it raises concerns when multiple messages are being displayed due to clarity. A tablet was labeled as “cumbersome to carry around all the time” and that it “should fit into the pockets” [55]. Worker suits can be also used to collect data for the ergonomic analysis [38]. A wearable mapping suit can be used to combine prototyping and body storming techniques and human-centered development to enable ideation for wearable technology directly on potential user [56].

The motion capture method can be provided by the complement of the optical and inertial sensors. This is real-time and portable motion capturing that avoids traditional outside-in systems due to occlusions and incorrect installation. It can be run on consumer mobile devices, which provides a convenient and low-cost way of ergonomic analysis. A self-contained motion capture system can be applied to perceive workers’ activity on the shop floor [57]. The use of motion capture systems and virtual reality is a promising potential but should be developed in such a way that both productivity increase and occupational safety and health principles are included [58]. Virtual reality (VR) can affect users’ health, which can be both positive and negative depending on the duration and intensity of use, as well as the user’s individual health conditions. The most common negative effects include seasickness, visual problems, or physical injury. In order to avoid these effects, it is necessary to ensure that the glasses and the ergonomic environment are properly adjusted and to take regular breaks [59]. For these reasons, to implement mocap and VR systems, the five steps are proposed: (1) input collection; (2) workplace design (to ensure well-being); (3) data collection (also anthropometric data for the digital twin); (4) data analysis with respect to productivity and ergonomics (KPIs (key performance indicators) and RULA and REBA (Rapid Entire Body Assessment) etc.); and (5) ergo-productivity satisfaction—the decision about whether productivity KPIs and ergonomic stores are satisfactory concerning the companies requirements.

A software application can help with the ergonomic evaluation of the workplace by setting up workplace metrics. Data on temperature noise and vibrations can be collected through a smart monitoring system and can be based on augmented reality information from the digital twin. The interaction with the system for the operator should be simple while working in the complex environment of Industry 5.0 [60]. Another proposed solution is using VR and implementation within the Unity 3D engine, which is reasonably priced. This provides an alternative look towards the working process and tool for managing workplace environment and processes. It can also be used for designing entirely new environments and training new employees [61]. VR can also be used to study the ergonomics of the human worker without compromising their safety. Motion data can be transmitted to the Perception Neuron v2 MoCap system, which allows for real-time ergonomics analysis. Ergonomics is then measured with the OWAS method [62]. However, virtual reality can also be used in the workplace as a means of improving employee well-being, particularly for those who work in challenging environments and may experience high levels of stress and poor well-being [63]. In addition, augmented reality (AR) can serve as an intelligent tutoring system to support operators in complex human–machine interactions. The Sophos-MS system enables operators to receive real-time feedback and augmented reality content on tasks to minimize the risk of accidents and a personal digital knowledgeable assistant to gain data, information, or knowledge. Augmented reality applications are suited to augment operators’ skills and abilities to perceive and act within the working environment [64]. In addition to its role as an educational tool, augmented reality (AR) offers a wide range of applications in the work environment. It can improve communication and collaboration by allowing remote teams to collaborate in real time through shared virtual objects and environments, making it easier to communicate and coordinate. In this context, the AR-CAM framework was also developed, which introduces a technical architecture for value co-creation and integrates advanced technologies such as digital twins (DTs), AR, constructive manufacturing (CMfg), and additive manufacturing (AMfg). This comprehensive system is designed to minimize barriers to understanding between different stakeholders and promote more effective collaborative partnerships [65]. In addition, AR can support operators in maintaining and repairing equipment by displaying instructions and schematics directly on the equipment. This approach allows operators to diagnose problems and make repairs more efficiently [66]. AR and wearables can enable us to bridge the skill gap to improve the efficiency and effectiveness of the staff through live guidance. AR continues beyond mobile apps for 3D object superimposition. AR can tackle challenges in complex CPS environments: (1) development of intelligent assistance systems for learning and performance assessment at the workplace, (2) job profile adaption, and (3) addressing the issue of work–life balance. AR technology can sustainably improve employability by providing access to better-paid jobs. It will also have a positive effect on well-being, enabling more (especially young and old) people to keep their jobs through up-skilling while job performance requirements increase [67]. The summary of the key findings of the research results related to wearables and hardware can be found in Table 4.

3.5. Aging Population

Thanks to advances in healthcare and improved living conditions, people around the world are living to an older age, and it is predicted that by 2030, one in six people will be over the age of 60. By 2050, the number of people over 60 will double to 2.1 billion and the number of people over 80 will triple to 426 million between 2020 and 2050. This demographic shift will bring significant changes not only in the population but also in the working environment. With the increasing proportion of older workers in modern work environments, new challenges are emerging that can affect their performance and well-being [68]. The aging workforce relates to reduced flexibility and strength and greater experience of the older operators [58]. It is crucial to implement strategies that not only take into account changes in worker flexibility and strength but also optimize working conditions and leverage their extensive experience to maximize productivity and safety [69]. The aging workforce is challenging in terms of physical, cognitive, ergonomic, and well-being aspects in Industry 4.0 and 5.0 environments [70]. The pathways towards the aging population-related challenges are the collection and analysis of new real data concerning the effects of aging on manufacturing systems, design of new assistive technology solutions for aging workers, provision of new age-oriented quantitative models for production management and control, development of new age-friendly workspaces by efficiently integrating Industry 4.0 smart solutions at an affordable investment cost, and finally, provision of new criteria and models to study human–robot interactions [71]. The integration of advanced robotics, artificial intelligence, and collaborative technologies can not only compensate for the physical limitations of older workers but also harness their experience and knowledge more effectively. Personalized assistive technologies and ergonomically designed work environments can further enhance their ability to contribute to complex production tasks, ultimately contributing to the sustainability of the labor market, even in an aging population [72]. Older workers have a higher tendency towards muscle fatigue, and they also learn slower than their younger colleagues. Therefore, real-time monitoring can lead to the avoidance of potential critical issues while the workforce is required to evolve into smart operators. On the other hand, the system with all modifications should remain sustainable. The authors distinguish four sustainability types—behavioral, mental, physical, and psychosocial. In behavioral sustainability, the management provides a safe workplace and human operators must be cautious to avoid injuries. Mental sustainability is related to physical stress and fatigue, which is also connected with the cognitive abilities that are related to the age of the worker. Physical sustainability is also connected to the age group—while older workers should avoid injuries by lifting heavy loads, the younger worker should also not be overloaded to avoid chronic injuries. Psychosocial sustainability contains the interaction factors in human–robot/machine collaboration, which can affect the emotional state of the operator [70]. The summary of the key findings of the research results related to the aging population can be found in Table 5.

3.6. Collaborative Robotics and Human–Machine Relations

Safety is aimed at the avoidance of the consequences of unexpected and unwanted collisions between humans and robots. Close collaboration could also provide psychological stress to humans, which impacts their well-being and performance due to unknown robot behavior. Therefore, cognitive ergonomics becomes a necessity as it deals with minimizing mental stress and psychological discomfort [73]. Safety considers no undesired contact between the robot and human, for which control algorithms are crucial for preventing collisions, while it can also be provided by the limitation of maximum permissible forces or torques and speed reduction [74]. Integrating technologies such as augmented reality or collision detection enables the further support of the operators’ interactions with collaborative robots in real time [75]. In this context, a planning approach has been proposed for situations where a human intervenes in variable and non-deterministic moments in the robot’s actions. This approach combines offline proactive planning based on human and robot availability predictions with an online reactive approach that adjusts the plan according to actual conditions. The proactive–reactive approach proves to be more effective than purely proactive or reactive approaches because it better balances idle time and overall production time [76]. 5G technology is a necessity in human–robot collaboration systems, AR-assisted operations, and data-driven interaction with the digital twin [42]. Including human–machine interaction in automatization provides better results than technology-centered models [77]. Collaborative systems are more complex than traditional ones as interaction leads to some advantages and disadvantages. How the operator feels about the installed work cell influences the overall performance of such an industrial application [78]. Multicriteria decision support methods can enable the evaluation of the conversion of a manual assembly workstation into a collaborative human–robot work cell. This kind of approach can support SMEs in the self-evaluation and adoption of collaborative systems. Analysis of the current situation can be provided by data collection, direct practical tests, interviews, technical reports, and documentation. Following data elaboration through the application of the HRAA algorithm, the subsequent evaluation encompasses the technical, safety, ergonomic, qualitative, and economic feasibility of the conversion process. The allocation of activities is determined based on a combination of four hierarchical evaluation indexes: (1) Technical Evaluation Index (TEI): this evaluates whether an activity can be efficiently performed by a robot, taking into account technical limitations; (2) Safety and Ergonomic Evaluation Index (SEEI): this assesses whether an activity poses physical stress to the operator or is potentially hazardous for humans and/or the production environment; (3) Qualitative Evaluation Index (QEI): this gauges whether an activity necessitates improvements in process quality, focusing on standardization and reducing process instability or variability; and (4) Economic Evaluation Index (EEI): this evaluates whether an activity contributes economic value to the final customer through the reduction of non-value-adding activities and overall cost reduction [79]. Individual designs of collaborative workplaces enable better output in terms of both the productivity and efficiency of workers [80]. Another approach to job quality-related factors with relevance to human–robot collaboration is measuring the cognitive workload, collaboration fluency, levels of trust, acceptance, and satisfaction. Predictable robots can take into account operators’ preferences. The research results have shown that a short distance between the user and robot and straight motion trajectories can lead to a high cognitive workload, increasing fear, surprise, and discomfort. The cognitive workload is also affected by workers’ awareness of robots’ speed of movement. In contrast, cobotic collaboration has been shown in other research to improve the quality of results and reduce workload in complex tasks. At the same time, the acceptability of cobots between people with no previous experience and those who had just used them was also analyzed, with both groups showing high levels of trust and enjoyment [81]. The higher the trust, the better the teamwork, but also the more the operators’ willingness to collaborate with the robot. Proactive human–robot collaboration can be achieved by bi-directional empathy and a holistic understanding [82]. The human–robot collaboration by the physical distance of collaboration can differ in four levels: (1) spatially separated workplaces divided into two zones, (2) spatially separated workplaces with an additional cooperation zone, (3) shared workspace and shared task, and (4) shared workspace with joint task and direct physical contact. The perceived proximity level influences human decisions. Moral decisions seem to be more utilitarian when a person is confronted with injury dilemmas than with life/death dilemmas, indicating a tendency to decide more rationally in non-life-threatening situations. A smaller spatial separation between humans and robots leads to more utilitarian decisions. As the cybersystem or cognitive system of the machine may have different levels of maturity and possibilities to act in an intelligent, context-specific, and adaptive way, (moral) decisions might be influenced through cognitive attributions [83]. This is why the design process demands multidisciplinarity by the principles of user-centered design. The aspects of economic viability, guarantee of effective participation of operators, acceptability and useability of robotic cell by future users, technical feasibility, and safety should be examined [84].

Robotic workstations can be assessed by the Rapid Upper Limb Assessment, Revised Strain Index, Key Indicator Method, and a well-being-indicating questionnaire. An ergonomic-leaning study of this kind was shown to be an efficient method to implement and assess collaborative workstations, enabling the continuous improvement of the processes. This kind of leaned approach relies on human-centered principles [85].

The collaborative robots could be more used in more demanding environments, which therefore require a higher degree of automation [86]. Novel communication levels between the humans and machines again raise certain security concerns, while the intense data exchange increases the risk of cyberattacks [87]. There is also cultural evidence of the impact of culture on the implementation of collaborative robots. In IoT technologies, US and Germany pursue IoT with the aim of autonomous control, while Japan prioritizes robot–human collaboration, mostly due to cultural differences. In Japan, people hold a philosophy that humans learn a body of knowledge over time and increase proficiency by repeating the same tasks, ending up being multi-skilled workers, so robots complement workers [88]. The implementation of collaborative robots (cobots) in manufacturing processes is a response to the fifth industrial revolution and the need for customized mass production. For this reason, a decision framework was proposed that would combine quantitative and qualitative criteria for the effective integration of cobots in manufacturing companies. Based on a qualitative analysis of interviews with key actors, new and complex decision factors were identified. The study proposes a decision framework based on a weighted scoring method that can be adapted to the specific needs of the enterprise. The key contribution is to facilitate a comprehensive and sustainable decision analysis in the implementation of cobots [89]. Furthermore, a validation study was conducted to assess the effectiveness of the CSRAT tool, which was developed as a comprehensive tool for assessing the safety readiness of collaborative robots, including five dimensions and 23 risk factors. The study included a web-based survey and analysis of opinions obtained from focus groups. The results confirmed that CSRAT is an effective tool for assessing the safety readiness of cobots. Participants particularly highlighted its value in preparing for cobot installation and identifying associated risks [90]. The summary of the key findings of the research result related to the collaborative robotics and human–machine relations can be found in Table 6.

3.7. Human Factors and Ergonomics (HF/E—General)

Human factors and ergonomics (HF/E) is an interdisciplinary field that focuses on the study of the interaction between people, technology, and the environment in which they work or live. The goal of HF/E is to optimize system elements to increase efficiency, safety, and comfort while minimizing errors, stress, and fatigue. This approach has been applied in a variety of sectors, including industry, healthcare, transportation, and information technology and encompasses a wide range of aspects such as work environment design, work organization, cognitive ergonomics, and physical ergonomics [91]. In human-centric systems, the synergy between the micro- and macro-ergonomics and a holistic approach is important for the development and implementation of the new technologies, but also in its incorporation in long-lasting corporate strategy. The use of mobile applications as a screening tool is a way to create healthy work conditions, which are so-called quick risk assessment tools (QRAs) [92]. To master both technical and human factors and ergonomics aspects of the production, the maturity level should be acknowledged, but the company should be aware of possible pitfalls when implementing new strategies. The four possible scenarios can be an outcome where: (1) technological maturity develops positively while the HF/E maturity does not change, resulting in a non-optimal utilization of the technology and exposure of the personnel to different types of health and safety hazards; (2) HF/E maturity develops positively, but technological maturity fails to develop when highly skilled personnel works with technologies that do not support their competence, which leads to a decrease in productivity and challenge in personnel motivation and commitment to work; (3) high technological maturity is achieved, but HF/E maturity decreases, resulting in a non-optimal use of technologies and possible hazards to human health and safety; and (4) technological maturity decreases, yet the HF/E maturity develops into a high level, resulting again in non-optimized production and challenges in personnel motivation and commitment to work. This holistic approach, which is micro-ergonomics and macro-ergonomics, is still an area that has not been studied profoundly in the manufacturing sector [93].

Effective algorithms and heuristic solution procedures can enable fatigue mitigation while integrating fatigue into scheduling problems by extra variables, i.e., the number, placement, and duration of rest breaks and the length of shift. The scheduling problems include minimizing tardiness penalty costs, minimizing the difference between workload and assignments, and minimizing the number of part-time workers. This kind of approach can reduce the occurrence of work-related fatigue and integrate the planning of workers and the performance of the machines [94]. There are indirect costs related to hiring, training, reduced performance, increased errors, increased scrap costs, and wasted managerial effort among the many indirect costs aspects related to employees’ MSDs in manufacturing. The application of human factors would improve productivity, technology implementation, quality, and system reliability [95]. Participatory ergonomics involves workers in ergonomic analysis and design. This can lead to the prevention of musculoskeletal disorders and injuries but also to decreased production time and reduced costs [96]. Physical and cognitive load enables integrated complexity evaluation methods of the production process. It has the flexibility to evaluate changes in labor loads. It includes operation difficulty, the complexity of information processing, time stress, and other factors needed to complete the production process. The output is the evaluation index and provides useful information for task assignment, operator selection and training, work organization, and performance prediction [97]. Another study focused on implementing a fatigue model that uses exponential and logarithmic functions to estimate its effect on human operators in a human–robot collaborative environment. Different assembly station collaboration scenarios, including pure human operators, robot support, and combined approaches, were evaluated using simulation in AnyLogic. The results showed that the integration of robot support and F-WS systems significantly increases the speed and efficiency of the operator while reducing fatigue accumulation. The study highlights the importance of integrating human–robot collaboration, intelligent task allocation, and ergonomics for sustainable development [98].

The implementation of lean management could lead to the neglection of human factors and ergonomics. Hard lean practices can worsen workers’ quality of work life, which influences performance. They therefore suggest soft lean practices and highlight the significance of human factors and ergonomics, which can lead to the sustainability of the model. The results of their work showed that the degree of lean performance is positively related to HF/E, while psychosocial and physical factors influence lean performance enhancement more than others do; cognitive factors influence them the least. The more complex a task and the more HFs were involved, the more difficult it is to substitute technology for the operator [99]. Job rotation can affect physical factors positively, i.e., MSDs, while negatively affecting psychosocial factors such as health at work, job satisfaction, and workers’ intention to stay on the job. This helps to expand job knowledge, work experience, and social support through interaction with more co-workers. Job autonomy, on the other hand, allows workers flexibility in scheduling their work and freedom to make decisions and select the methods used to accomplish their tasks [82]. Cognitive factors such as situation awareness, human reliability (human error), and decision-making skills are positively related to operational performance [100]. Physical ergonomics has more influence on the enhancement of lean performance than organizational ergonomics. Cognitive ergonomics have the least influence on lean performance enhancement [101]. Lean management [102] affects workers’ psychosocial aspects more than physical factors, but cognitive ergonomics has a positive association with lean performance; however, this indicates that the enhancement of workers’ situation awareness, reliability, and decision-making skills lead to better performance, which can be achieved by corresponding ergonomic interventions [103]. However, the integration of Lean Six Sigma (LSS) methodology and ergonomics for continuous improvement in organizations has been suggested. Although this approach is complex, its implementation benefits both organizations and their employees. The results show that LSS methodology and ergonomics are key to sustainable performance improvement in organizations [104].

Human factors that are crucial in workplaces today are: physical fatigue, attention, mental workload, stress, trust, and emotional state. Each of these brings specific human psychological responses, affecting the human brain, cardiovascular, electrodermal, muscular, respiratory, and ocular reactions, which are valuable indicators of human states and can be recognized by changes relevant to human organs and tissues such as the brain, heart, skin, blood flow, muscle, facial expressions, voice, etc. Cognitive workload provokes similar reactions, specifically seen in heart rate changes and electrodermal activity. Wearable technologies offer a more integrated and holistic approach for measuring human factors [105]. The human-centric system demands new ergonomic approaches in the context of new technology used. One of the developed models is the HCV model, which enables the understanding of the management of the human–machine interaction from a sociotechnical perspective and predicts the possible reactions of the employees to develop the company’s strategic direction. The HCV model includes: (1) human (identity awareness, physical, and spiritual aspects), (2) technology (defined by the strategic management system and internal work environment), and (3) organization (perception of the employee’s value inside the organization). There are elements of connection: (1) the belonging (social and societal aspect), (2) the working conditions (workplace and work environment design), and (3) the external environment (organizational readiness to predict changes and act sustainably in complex situations) [106]. The ergonomic aspects of cyberspace in Industry 5.0 should also be studied. Increasing psychological distraction, digital footprint, privacy issues, cyberattacks, and other issues are one of the main concerns in the digital work environment, so cyberergonomics as a term is proposed. It addresses both the ergonomic aspects of human cyber life and cyber work. The prefix cyber has a wide range of meanings that are mostly related to digital technologies, and it refers to the ergonomics of advanced cybertechnologies and combines cyberscience capabilities to address the ergonomics goals in the optimization of individuals’ safety, productivity, and health. Cyberergonomics can be especially useful when addressing the difference in work approach among different age groups [107].

The motion analysis system aims at human body digitalization and movement simulation in an industrial environment. It integrates motion capture (MoCap) technology and specialized software for ergonomic analysis provided by OWAS, REBA, NIOSH, and EAWS. The output data are the following: time and space analysis of the workplace area, hand displacement over time and velocity trend, cumulative vertical movements for lifting and lowering, and control volume analysis to distinct value-added and non-value-added activities [108]. The MoCap system can be used to digitalize the ergonomics analysis tool Key Indicator Method (KIM), which can cover only one part of the ergonomics assessment, which is body posture, without including the time and load weighting [109].

Supervisor support and role clarity have been shown as important for the employee’s well-being. One of the possible solutions is the portion of collective awareness with several communication methods such as videos, posters, etc., as short and simple messages about the new tools and technologies. The fear of job loss can also be communicated through this kind of communication [110]. Individual worker preferences awareness could improve motivation, resulting in higher overall performance. Planning models of Industry 5.0 can have an individualized user interface so the operators can make tailor-made decisions, and their pleasant working conditions can be predicted through mathematical models. To avoid monotonous work, well-designed breaks and rotational job schedules should be provided [111]. Human factors in the design process are rarely used in the practice, which impacts the safety and resilience of human workers. This is mostly due to a lack of non-technical skills in workers, so the validation and verification of human factors should be provided from the start, and the authorities should check back the regulatory framework of communication of the human factors framework. The missing focus is also aimed towards management thinking, and the solution for straightening the framework is via media and education [112]. The summary of the key findings of the research result related to HF/E can be found in Table 7.

3.8. Occupational Health and Safety

In the context of Industry 5.0, occupational safety and health (OSH) is becoming an increasingly complex and important topic, as people and technology are closely interconnected and interdependent in this environment. The human–machine collaboration requires new approaches to OSH that can take into account not only traditional risks but also new risks arising from the interactions between people and technology [113]. There is not a single solution for all developed and applicable scenarios. Traditional OHS safety analysis methods cannot analyze socio-technical issues collectively and falls short in identifying and assessing emergent system properties, whereas complexity methods respond to the absence of approaches that can assess tightly coupled intractable issues. In complexity-thinking methods, humans, machines, and interfaces need not be decomposed into disintegrative units of analysis. Contrarily, the methods are capable of taking the joint problem-solving ensemble as the unit of analysis [114]. Proactive activity monitoring enables the safety of workers on the shop floor. Detection of falling objects, movement of humans, and abnormal vibrations can be detected, which can especially be useful when work is being performed in isolation with no available emergency assistance. Such systems are supported by Internet of Things and machine learning, which increase the accuracy of detection [115]. Again, it is important to ensure that data and systems are protected to prevent unauthorized interference that could compromise the safety of workers [116]. The summary of the key findings of the research result related to occupational health and safety can be found in Table 8.

3.9. Well-Being

Industry 5.0 emphasizes not only technological innovation and automation but also the well-being of employees. This includes promoting physical and mental health, work–life balance, and providing meaningful and creative work that supports personal development and a sense of fulfilment [117]. The emphasis on inclusiveness and diversity allows for the creation of work teams with diverse perspectives and skills that contribute to innovation and effective problem solving. This approach not only increases the productivity and quality of work but also contributes to the overall well-being of employees by fostering their engagement, creativity, and satisfaction with the work environment [118].

Well-being is a priority as well as creating rewarding and motivating work environments that suit operators’ needs. Lean management also puts the human in the center of the system, while in the future, the systems tend to shift from technological to socio-technological systems, which have a demand to upgrade the knowledge and skills of workers. Operator 5.0 can be defined according to its purpose, i.e., a self-resilient operator that has evolved in the face of its inherent weaknesses and adversities, and an operator focused on system resilience, i.e., resilient human–machine systems [119]. This is why well-being in manufacturing demands a transdisciplinary approach, which is also needed to measure and promote social sustainability. Internet of Things is a good tool to provide a redesign of manufacturing towards a human-centered system. This has a positive effect on humans and their health, satisfaction, and performance. The IoT framework of well-being measuring enables measurement of physical, cognitive, and environmental aspects. Although this is shown as a good principle, it has yet to be proven in a real working context [6].

There are several methods to evaluate workers’ experience, which leads to the improvement of workers’ well-being and the company’s performance. There are six macro-categories of risk factors: (1) awkward posture, which leads to physical damages of the body; (2) workspace—having an inadequate workstation (workers are unable to operate within the recommended zones); (3) work activity—risks deriving from manual handling and movements; (4) work organization analyses related to organizational choices and their impact on the overall experience; (5) work environment—all environmental factors that indirectly affect the operator’s activity; and (6) tools and devices—aspects related to HMI that affect the cognitive domain [120]. Among the physical workplace optimization, the training sessions are proposed to improve the risk awareness and skills of the worker but also to reduce cognitive effort during the task execution. Based on that, several KPIs were identified to measure the improvement [121]. On the other hand, there is a conflict between operational performance and employee well-being, mostly due to a lack of standards and practical procedures. This is an obstacle to human-centric approach adoption; therefore, the adoption of digital tools was proposed to “anticipate ergonomic analysis during process design in terms of layouts and tasks”. This leads to the definition of a structured procedure for automatic data extraction from the virtual analysis and set of indicators measurement for manufacturing ergonomic assessment. A new ergonomic method based on virtual analysis and EAWS (Ergonomic Assessment Worksheet) model was created. This enables preventive ergonomic workstation assessment with digital tools such as human modeling software and advanced VBA-coded Excel sheets [122]. The ability to be included in decision-making influences the pleasure and motivation of daily work. Active participation demands the learning of certain non-technical skills, such as project management of systematic problem-solving. Adequate information on changes reduces the level of frustration, but the implementation of new technologies might cause division in the work environment. The evidence from the practice shows that once the digital solution is fully implemented and standardized, the workers experience improvement in well-being, while improvements were found in both physical and cognitive ergonomics. In the transformational phase, well-being is affected both positively and negatively. Negative effects are mostly related to the fear of change and a new position in the company, while positive effects consist of an excitement about the use of new technologies. Perceived well-being and performance are in a neutral position in the Before phase, worsen in the During phase, and improve beyond the neutral Before phase in the After phase [6]. The summary of the key findings of the research result related to well-being can be found in Table 9.

4. Discussion

The role of humans within the manufacturing system of Industry 5.0 is directly related to the business value increase [123]. The novel digital technology can be utilized to its full potential with the support of the human, who uses it as an assistant to upgrade its possibilities [119]. The holistic approach integrating physical, cognitive, and organizational ergonomics improves efficiency, safety, and comfort while minimizing stress and errors [13]. In human-centric systems, incorporating advanced technologies such as wearable devices, motion capture systems, and cyberergonomics offers a pathway to enhanced workplace adaptability, particularly in the context of Industry 5.0 [107]. Participatory ergonomics and models like HCV contribute to a deeper understanding of human–machine interaction, fostering resilience, sustainability, and strategic alignment [96]. However, the effective integration of HF/E in technological and organizational strategies requires addressing significant challenges in implementation, evaluation, and worker engagement.

While physical, cognitive, and organizational ergonomics are well studied, the concept of cyberergonomics, which addresses digital distractions, privacy, and cyberattacks, remains underexplored. Existing tools like OWAS, REBA, and NIOSH focus on specific aspects of ergonomics (e.g., posture) but do not provide comprehensive evaluations that integrate physical, cognitive, and psychosocial factors. Despite the importance of HF/E, there is insufficient practical application and validation in workplace design, partly due to workers’ lack of non-technical skills and gaps in regulatory frameworks. The integration of HF/E into Industry 5.0 systems is often fragmented, with a need for unified frameworks that align technical, human, and organizational factors.

4.1. Operator 5.0

Industry 4.0 has introduced Operator 4.0, a smart, healthy and social operator whose role is to control, design, and upgrade the new digital system [64]. Operator 5.0, on the other hand, uses real-time data and information from the manufacturing for optimal and continuous decision-making while being in the center of the production system [124]. This is a technology-enabled role where the collaborative systems are adapted to individualized needs to utilize everyone’s best capabilities and increase the level of motivation, which reflects the overall productivity and system efficiency [8]. The lifelong learning concept enables human to be more secure in the behavior of the collaborative systems and therefore gain confidence in their work environment to provide a healthy and safe workplace [125]. Training is constant with real-time feedback from the user, which can be subjective, but with certain standardized control methods available from the management, the adaptation to the human needs can be provided effectively [42]. Operator 5.0 therefore represents a paradigm shift towards highly skilled professionals who collaborate with intelligent systems, leveraging cognitive, sensorial, physical, and interaction capabilities to enhance safety, efficiency, and decision-making. The integration of technologies such as artificial intelligence, digital twins, wearable devices, and real-time data systems underscores the shift towards a human-centered Industry 5.0 that prioritizes sustainability, well-being, and collaborative innovation. However, it was noticed that there is insufficient research addressing the integration of sustainable practices and system resilience in the Operator 5.0 framework and that is related to the need for personalized systems tailored to individual operator needs, with limited insights into how such systems can be effectively designed, implemented, and scaled. While active involvement of operators in system design is emphasized, there is little detail on methodologies or frameworks to ensure their meaningful participation. Although the papers [11,124,126] highlight the risks of information overload from advanced systems, the lack of strategies to mitigate these challenges or enhance the operator’s cognitive capacity to manage large-scale data effectively is noticed. The ethical implications of a novel work environment for Operator 5.0 are recognized but as of yet are not sufficiently explored. Addressing these limitations requires targeted research into sustainability integration, personalized system design, cognitive load management, and ethical considerations. This would not only advance the Operator 5.0 framework but also ensure its alignment with the broader objectives of Industry 5.0 and Society 5.0, fostering a future where technology and human creativity work in harmony.

4.2. UX Design and Leadership

The shortage of workforce in general, which now adds up to the demand for new people skills and knowledge, is one of the challenges when implementing the human-centric system and keeping productivity at the desired level; the learning curve should be minimized as much as possible [21]. This can be enabled by a special interface design for the users and individualization of the workplace but also with a shift in management approaches, which now should be employee-centered with soft lean practices, as hard lean practices have shown to hurt human work [127]. This kind of diverse workplace demands the introduction and nourishment of novel skills, such as emotional intelligence and cognitive flexibility, which can lead to the balance between the worker’s well-being and high system performance while fostering creativity towards continuous improvement [20]. Combining leadership with UX design enhances the ability to create systems that address employees’ needs and improve user experiences. Human-centered design principles, incorporating ergonomics, cognitive flexibility, and emotional intelligence, enable organizations to foster inclusivity and adaptability. The adoption of assistive and collaborative technologies, such as motion capture (MoCap) systems and AI, provides a foundation for optimizing ergonomics, safety, and performance. Again, ethical considerations, including privacy, trust, and justice, must be addressed to ensure responsible technology use. The difference between human-centered and user-centered design is noted, but the lack of empirical metrics and maturity models for assessing human-centeredness in workplace systems remains a challenge, in which the role of non-technological interventions in improving ergonomics, teamwork, and satisfaction is underexplored, creating a bias toward technological solutions. There is a potential for developing comprehensive frameworks for human-centered design, exploring the socio-psychological impacts of automation, and establishing ethical standards for emerging technologies. Practical solutions for inclusivity, sustainability, and collaborative decision-making should be prioritized.

4.3. Digital Human Modeling

Traditional ergonomic methods are enriched by the possibilities of the digital twin, which is now emphasized with digital human modeling by the individual anthropometric values of a single human worker, but also by the data collected from their work, which not only allows for visualization but also optimization due to the possibility of behavior prediction [128]. By leveraging tools like JACK, HumanCAD, Siemens Tecnomatix, and virtual reality-based systems [17,39,40], organizations can perform detailed ergonomic risk assessments, optimize workspace designs, and enhance worker safety and efficiency. These technologies support the evaluation of posture-related risks, simulation of biomechanical loads, and optimization of user-centered workspaces, fostering a sustainable approach to design. Integrating advanced technologies such as motion capture systems and virtual reality further enriches the analysis, providing immersive and interactive experiences for both real-time and offline ergonomic assessments [36,37,41].

Despite the significant potential of digital human modeling, several limitations highlight existing scientific gaps. There is a need for precise reproduction of external anthropometric dimensions and their linkage to internal skeletal structures. Inaccuracies can result in significant errors, reducing the validity of biomechanical analyses and potentially causing ergonomic risks. While current tools support various ergonomic standards (e.g., RULA, OWAS, NIOSH), they lack advanced capabilities for muscle force estimation and joint load analysis, restricting their application in detailed biomechanical investigations. Many such tools require high technical expertise and specialized equipment, posing challenges for widespread adoption, particularly in small and medium-sized enterprises (SMEs). Advanced systems and motion capture setups involve substantial costs, limiting their accessibility for broader industrial applications. By addressing these limitations, digital human modeling can become a more accessible and reliable tool, advancing ergonomic design and fostering safer and more efficient working environments.

4.4. Wearables and Hardware

Assistive technologies such as AR, VR, exoskeletons, or wearable sensors provide the needed data but also simplify the learning for the user while on the other hand raising certain ethical concerns, which is one of the future challenges to be resolved not only within the company, but also on the higher regulatory level [93]. Along with digital human modeling, the integration of wearable devices, augmented reality (AR), and virtual reality (VR) into ergonomic analysis and workplace design represents a significant step toward enhancing worker well-being and ergonomic workplace optimization [65,66,113]. Tools such as motion capture systems, smartwatches, AR-assisted systems, and VR provide actionable insights for optimizing workplace design, fostering skill development, and improving safety [58,63,122]. However, while these advancements hold promise, their full potential has yet to be realized. Wearable devices and motion capture systems face challenges in measurement accuracy, particularly in environments with poor lighting or during complex movements (e.g., arm crossing or trunk rotation). Limitations in wrist joint tracking and issues of self-occlusion in certain postures reduce the reliability of collected data. The integration of wearable devices, AR, VR, and digital twins remains a complex task due to the diversity of data formats and technologies. Also, it is noticed that there is a lack of standardized protocols for seamless interaction between different systems and tools. Devices like smartwatches and tablets have usability concerns (e.g., message clarity, portability); similarly, AR and VR solutions often require advanced technical setups that may be cumbersome for widespread implementation. VR usage can lead to negative health effects, such as seasickness and visual fatigue, especially with prolonged exposure. The use of AR and VR in high-stress environments risks over-reliance on technology and worker burnout. The psychological effects of immersive systems on long-term well-being are not well understood.

4.5. Aging Population

The age gap is another important topic to be addressed in the novel human-centric systems [58]. While the older generation tends to have challenges in accepting novel technologies and are more afraid that the technology will replace their work, younger workers have a novel view of the work organization and work–life balance, and this is another place where emotional intelligence can be very useful for management skills to improve the productivity and maintain market competitiveness [71].

Industry 5.0 solutions offer opportunities to mitigate the physical limitations of older workers while optimizing their contributions. By integrating age-friendly workspaces, real-time monitoring, and sustainable practices across behavioral, mental, physical, and psychosocial dimensions, companies can foster a more inclusive and effective workforce, ensuring long-term sustainability and adaptability in an aging society [69,71,129]. While assistive technologies are proposed as a solution [58,70], there is a lack of specific, validated designs tailored to the unique needs and capabilities of older workers. Also, there is an insufficient collection and analysis of real-world data on the effects of aging on manufacturing systems, particularly in terms of productivity, error rates, and safety outcomes. Research on developing adaptive training systems that accommodate slower learning rates and other cognitive challenges faced by older workers is limited, while sustainability, categorized into behavioral, mental, physical, and psychosocial dimensions, proposes no unified framework to measure and implement these across diverse age groups effectively.

4.6. Collaborative Robotics and Human–Machine Relations

Collaborative technologies are the key of human-centric systems and the communication between humans and machines (robots) [130]. The familiarity with the machine behavior and the safety measures taken are the essential predispositions for the avoidance of human insecurities and later stress and frustration in collaborative work [106]. This is how the best work characteristics of both humans and machines can be utilized: a human as an irreplaceable and creative part of the work system and the machine as a constantly productive and precise part of the work system [35]. The need for customized production and enhanced workplace ergonomics while ensuring occupational health and safety is addressed [22,87], while cobots can reduce ergonomic loads, enhance task efficiency, and facilitate adaptability in complex environments, making them a cornerstone of the fifth industrial revolution [81,84,90]. Decision-making frameworks, such as CSRAT [90], help assess safety readiness and streamline cobot integration into manufacturing processes. However, the success of human–robot collaboration depends heavily on factors such as trust, predictability of robot behavior, cognitive workload management, and cultural context [81]. Limited research explores the influence of cultural differences on cobot implementation and human–robot interaction, particularly how cultural philosophies shape operator preferences and acceptance. User-centered design principles are discussed, but practical methods for ensuring effective operator participation, usability, and acceptability in robotic cell design are underdeveloped. Current optimization algorithms for task allocation need improvement to account for dynamic and non-deterministic human interventions in cobot workflows. The proactive–reactive planning approach remains underexplored in diverse real-world scenarios. While the importance of safety algorithms is emphasized, research lacks in-depth strategies for addressing cognitive ergonomics and minimizing psychological stress caused by unpredictable robot behavior. By addressing these limitations, cobots can become safer, more effective, and widely accepted tools, driving innovation and sustainability in modern manufacturing.

4.7. Human Factors and Ergonomics—General Approach

The integration of traditional human factors and ergonomics into various sectors, such as industry, healthcare, transportation, and information technology, has demonstrated its significance in enhancing efficiency, safety, and comfort [23,93,112]. Through the optimization of work environments, work organization, cognitive ergonomics [4], and physical ergonomics, HF/E offers a holistic approach [93] to system design and management. However, the findings suggest that human-centric systems demand deeper understanding and integration of cognitive, physical, and psychosocial aspects for technology to be utilized effectively. The synergy between micro- and macro-ergonomics, coupled with a holistic approach, is crucial for the successful implementation of new technologies and their alignment with corporate strategy [92]. Assistive technologies such as exoskeletons and smart gesture control systems have showcased their potential to improve working conditions and efficiency. Furthermore, quick risk assessment (QRA) tools, such as mobile applications, can play a pivotal role in establishing healthy work conditions [92].

However, the integration of HF/E into production systems presents challenges. The scenarios highlighting mismatched development between HF/E and technological maturity underline the potential pitfalls. For example, technological advancements without corresponding HF/E maturity can lead to unsafe work environments, while HF/E maturity without technological advancement can reduce productivity and worker motivation. These findings underscore the necessity for a balanced and synchronized development of both domains.

Participatory ergonomics [96], involving workers in the design process, has shown promising outcomes in reducing musculoskeletal disorders (MSDs), improving productivity, and achieving cost reductions. Additionally, the integration of lean practices and ergonomics [99,101] is crucial to balancing operational efficiency with employee well-being. Lean methodologies positively influence HF/E factors, but hard lean practices may adversely affect workers’ quality of life, necessitating a shift towards soft lean practices that emphasize psychosocial and physical factors.

Emerging areas, such as cyberergonomics [107], emphasize the need to address digital workspace challenges, including psychological distraction, privacy concerns, and cyberattacks. Cyberergonomics, combined with traditional HF/E principles, can provide a comprehensive framework for addressing the unique demands of Industry 5.0.

Advanced technologies, such as motion analysis systems and fatigue models [108], enable precise ergonomic assessments and improvements in human–robot collaboration. These tools facilitate the optimization of production processes by integrating operator well-being with machine performance [110,111]. Moreover, initiatives such as supervisor support, tailored training programs, and individualized user interfaces enhance employee engagement and mitigate the risks associated with technological transformations.

Despite these advancements, HF/E is still underutilized in the design and implementation phases, primarily due to gaps in non-technical skills and regulatory frameworks. To overcome this, the validation and verification of HF/E principles should be emphasized from the initial stages, supported by robust media and educational initiatives. Management’s role in integrating HF/E into strategic decision-making is critical for achieving sustainable and resilient systems.

4.8. Occupational Health and Safety

However, in the context of Industry 5.0, it is essential that as advanced technologies become more integrated into the work process, increased attention is also paid to system safety and worker health protection. Ensuring safety is not just a matter of technical measures but also involves implementing robust risk management systems that can anticipate and minimize potential threats in real time.

Occupational safety and health (OSH) has become a multidimensional challenge due to the close integration of human workers and advanced technologies such as collaborative robots, autonomous systems, and interconnected devices [113]. This paradigm shift necessitates new approaches to OSH that address both traditional risks and emergent socio-technical risks arising from human–machine collaboration. Technologies like IoT and machine learning further enhance proactive monitoring, enabling the detection of risks such as falling objects or abnormal vibrations, especially in isolated work scenarios [114,116]. From the literature review, it is noticed how traditional OSH analysis methods are inadequate for assessing the socio-technical complexities of Industry 5.0 environments. They fail to address emergent properties and tightly coupled human–machine interactions comprehensively. While complexity-thinking methods are proposed, their practical application in diverse industrial scenarios remains underexplored and requires further validation. Current systems lack advanced decision-making tools that provide real-time responses to emergent safety risks in dynamic human–machine environments. The interconnected nature of these systems also amplifies cyber threats, emphasizing the need for robust data security measures to safeguard worker safety and system integrity. Proactive monitoring systems for detecting physical hazards (e.g., falling objects) are discussed but lack detailed studies on their implementation, scalability, and reliability in complex industrial setups.

4.9. Well-Being

Employees are seen as vital contributors to organizational success, with a focus on physical and mental health, work–life balance, inclusiveness, and creativity [117,121]. Emerging tools, such as IoT frameworks and digital ergonomic analyses, offer promising avenues for redesigning manufacturing systems to prioritize employee well-being and social sustainability [118,120,122]. However, balancing operational performance with well-being remains a challenge, requiring structured procedures and advanced methodologies to optimize ergonomic and cognitive factors. While IoT frameworks for measuring well-being show potential, their practical application and effectiveness in diverse manufacturing environments remain largely untested. Current metrics for workplace optimization focus heavily on physical ergonomics, with insufficient indicators for cognitive and psychosocial aspects. The transformational phase of adopting new technologies impacts well-being both positively and negatively, but there is limited research on long-term outcomes and strategies to mitigate negative effects. There is a lack of standardized procedures to balance operational performance with employee well-being, creating barriers to the widespread adoption of human-centric approaches.

Overall, it is important that businesses in Industry 5.0 not only maximize productivity but also create a safe, healthy, and sustainable working environment for all employees.

5. Conclusions

Industry 5.0, as a resilient, human-centric, and sustainable system, demands a new design of workplace environment and approach to both humans and machines to enable the complete utilization of their potential. The human-centered work environment is characterized by the use of collaborative technologies, primarily collaborative robots that should be installed considering all of the safety measures. Also, the workstation should be designed to the ergonomic needs of a human worker, who must be familiar with robot behavior to reduce the stress level, which impacts productivity. The collection of real-time data from the human user with direct subjective feedback about the work conditions is another characteristic of a human-centered system, which is another method to utilize the possibilities of human workers. Among the life-long learning about the novel technology, humans should learn novel skills needed for operating in such a system, which are emotional intelligence and cognitive flexibility, to enable the diverse and individualized workplace environment, which answers RQ1. Along with physical safety for humans and workplace design by the traditional ergonomic principles, the safety of personal data should be provided with ethical issues being taken into consideration in human-centered systems. The understanding of human needs is crucial to provide the balance between the worker’s well-being and the needed productivity level, while in improving the productivity and efficiency of human workers, the cognitive load should be measured by the principles of cognitive ergonomics. Digital human modeling enables the proper simulation of workers’ anthropometric aspects for ergonomic optimization, while data from the various sensors analyzed within the digital twin can be used to predict human behavior. Along with the safety dimension, the company should take into consideration the cognitive human dimension and wider societal dimension, which enables the achievement of the sustainable system, which answers RQ2. The human factors and ergonomics are now very important segments of the manufacturing systems. The principles of cognitive ergonomics are one of the main enablers of a human-centered work environment, which are used and combined with the traditional ergonomics methods. Other enablers are assistive technologies such as exoskeletons, AR, VR, smart glasses, and similar methods, which assist the human in operating complex tasks, but also include technologies serving as a collector of data about both human and machine conditions, which enables the prediction of future behavior. Unfamiliarity with the technology, human insecurity in its use, lack of novel skills, and knowledge, but also the age gap, are recognized as some of the main barriers to the human-centered work environment, which answers RQ3. The detailed overview of the enablers and barriers is shown in Table 10.

Based on this research, several pathways towards potential future work are recognized. One is the formation of the objective human feedback measuring method of their work conditions, which would also enable process optimization. Second is the development of a method for overcoming the age gap in manufacturing and establishing an individual learning approach for different worker groups, but also defining individual needs to maintain high productivity within the collaborative systems. Third is the development of joint traditional and cognitive ergonomics measuring methods with potential implementation in digital twins. Fourth is related to exploring the impact of the work environment on workers’ mental health in Industry 5.0, which could include research aimed at understanding how new technologies and working conditions affect workers’ mental health. A study could examine stress factors associated with working with automated systems, isolation when working with robots, or rapid technological change. The goal would be to develop interventions and support mechanisms to help workers better manage these challenges while exploring ways to design work environments to promote psychological well-being.

Author Contributions

Conceptualization, M.T.; methodology, M.T. and A.B.; software, A.B.; validation, M.T., A.B. and T.O.; formal analysis, M.T. and A.B.; investigation, M.T., A.B. and T.O.; resources, H.C.; data curation, M.T. and A.B.; writing—original draft preparation, M.T. and A.B.; writing—review and editing, M.T. and A.B.; visualization, M.T. and A.B.; supervision, T.O. and H.C.; project administration, M.T.; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. PRISMA flowchart.

Figure 2. Number of papers considered in this review published through the years.

Table 1

Key findings—Operator 4.0/5.0.

Title	Authors	YearPublished	Key Findings
From Industry 4.0 towards Industry 5.0: A Review and Analysis of Paradigm Shift for the People, Organization and Technology [8]	M. C. Zizic, M. Mladineo, N. Gjeldum, and L. Celent	2022	Transformation of operators into active partners with advanced systems (Operator 4.0), which enhances cognitive, sensorial, physical, and interaction capabilities through Industry 4.0 technologies, with roles such as augmented, healthy, smarter, social, and analytical operators supported by tools like XR, wearables, AI, and big data analytics, all guided by value-sensitive design principles.
Human-machine interaction towards Industry 5.0: Human-centric smart manufacturing [9]	J. Yang, Y. Liu, and P. L. Morgan	2024	Technologies enhancing operators’ capabilities include cognitive tools (cloud computing, simulation, VR, and AI), sensorial tools (health monitoring sensors, activity trackers, and IoT), physical tools (collaborative robots, exoskeletons, teleoperated systems), and interaction tools (human–machine interfaces, augmented reality, mobile devices, and personal intelligence assistants).
Toward human-centered intelligent assistance system in manufacturing: challenges and potentials for operator 5.0 [10]	C. Bechinie, S. Zafari, L. Kroeninger, J. Puthenkalam, and M. Tscheligi	2024	Workplace automation should prioritize human intelligence and tacit knowledge over full automation, focusing on systems that place human expertise at the center to maximize the collaborative potential between humans and machines.
Toward the industry 5.0 paradigm: Increasing value creation through the robust integration of humans and machines [1]	J. Ordieres-Meré, M. Gutierrez, and J. Villalba-Díez	2023	Operator 5.0 builds on human-centric principles influenced by human factors and ergonomics, emphasizing the integration of flexible architectures, human-centered processes, wearable devices with biosensing capabilities, and standardized workflows that combine automated and human-driven operations.
Transforming Quality 4.0 towards Resilient Operator 5.0 needs [11]	M. Hattinger and K. Stylidis	2023	Operator 5.0 represents a new generation of highly skilled professionals who combine expertise in advanced technology and data analysis with creativity, problem-solving, intuition, and experience to enhance production processes and product quality.
Current development on the Operator 4.0 and transition towards the Operator 5.0: A systematic literature review in light of Industry 5.0 [12]	B. Gladysz, T. Tran, D. Romero, T. van Erp, J. Abonyi, and T. Ruppert	2023	The transition to Operator 5.0 is inevitable, but the effective integration of human factors is hindered by insufficient exploration of new technologies, a lack of research on human-centered sustainability, and limited focus on system resilience and implementation challenges.
Roadmap to Industry 5.0: Enabling technologies, challenges, and opportunities towards a holistic definition in management studies [13]	M. Piccarozzi, L. Silvestri, C. Silvestri, and A. Ruggieri	2024	Industry 5.0 emphasizes sustainability and human well-being, aligning with the broader trend of Society 5.0 by integrating digital transformation with individual creativity while actively involving operators in design and development to meet their specific needs and preferences.
A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0 [14]	D. Mourtzis, J. Angelopoulos, and N. Panopoulos	2022	Key enabling technologies for Industry 5.0 include human-centric solutions, bio-inspired technologies, intelligent materials, simulation, digital twins, AI, cybersecurity, and energy-efficient autonomous systems, delivering benefits such as enhanced customer experience, hyper-customization, responsive value chains, interactive products, and reintegration of manpower into factories.

Table 2

Key findings—UX design and leadership.

Title	Authors	Year Published	Key Findings
A human-centric methodology for the co-evolution of operators’ skills, digital tools and user interfaces to support the Operator 4.0 [15]	G. Fabio, C. Giuditta, P. Margherita, and R. Raffaeli	2025	A people-centric approach in Industry 5.0, combining leadership strategies to motivate employees with UX design to optimize user experiences, is essential for enhancing employee satisfaction, productivity, loyalty, and overall business performance.
Industry 5.0 Beyond Technology: An Analysis Through the Lens of Business and Operations Management Literature [16]	M. Borchardt, G. M. Pereira, G. S. Milan, A. R. Scavarda, E. O. Nogueira, and L. C. Poltosi	2022	Involving employees early in the workplace design phase is essential for creating sustainable and resilient workplaces that meet human factors and ergonomics standards, ensuring health and safety in work conditions.
Digital twins to enhance the integration of ergonomics in the workplace design [17]	F. Caputo, A. Greco, M. Fera, and R. Macchiaroli	2019	Designers should account for worker population variability and anthropometry by utilizing wearable devices to collect data, which can be analyzed through simulations to inform design decisions.
Employee-centric innovation: Integrating participatory design and video-analysis to foster the transition to Industry 5.0 [18]	V. Orso, R. Ziviani, G. Bacchiega, G. Bondani, A. Spagnolli, and L. Gamberini	2022	Integrating employees’ self-reported data with objective event-based data from video analysis enhances understanding of work activities, supports the development of effective digital tools, and facilitates the adoption of portable and wearable devices while addressing potential privacy concerns.
Lean Six Sigma with Value Stream Mapping in Industry 4.0 for Human-Centered Workstation Design [19]	F.-K. Wang, B. Rahardjo, and P. R. Rovira	2022	Combining a human-centered approach with lean tools like visual management, error prevention, and waste analysis effectively reduces human errors.
Design of Human-Centered Collaborative Assembly Workstations for the Improvement of Operators [5]	L. Gualtieri, I. Palomba, F. A. Merati, E. Rauch, and R. Vidoni	2020	Incorporating human factors into planning models enhances system performance, improves employee learning experiences, and mitigates health hazards, addressing the shortcomings of traditional models that overlook these considerations.
The Competences Required by the New Technologies in Industry 4.0 and the Development of Employees’ Skills [20]	A. Firu, A. Tapirdea, O. Chivu, A. I. Feier, and G. Draghici	2021	Emotional intelligence and cognitive flexibility are essential skills for future jobs, enabling workers to adapt to high-level automation in manufacturing, with the strategic goal of fostering an agile workforce that continually develops its competencies.
Human factors in production and logistics systems of the future [21]	Sgarbossa, E. H. Grosse, W. P. Neumann, D. Battini, and C. H. Glock	2020	Workplace design should incorporate individualized human factor tools and assistive technologies to enhance diverse employee capabilities while maintaining age-friendliness and balancing automation with human tasks to avoid worker overload and “phantom profits”, fostering decentralized, autonomous, and data-driven management strategies.
Multi-objective collaborative assembly line design problem with the optimisation of ergonomics and economics [22]	M.-A. Abdous, X. Delorme, D. Battini, and S. Berger-Douce	2022	In production line assembly, optimizing investment costs and ergonomics, particularly fatigue and recovery, can be effectively addressed using a mixed integer linear programming formulation as a multi-objective solving algorithm.
Human-centricity in the design of production planning and control systems: A first approach towards Industry 5.0 [3]	P. Rannertshauser, M. Kessler, and J. C. Arlinghaus	2022	Human-centered design differs from user-centered design, as demonstrated by the link between cognitive biases and the design of product planning and control (PPC) systems, highlighting the need for standardized guidelines to improve the maturity of human-centeredness in these systems.
Human Factors in Production Systems Motives, Methods and Beyond [23]	P. Brauner and M. Zie	2014	Addressing human-centered design requires tackling the complexity of human cognition and decision-making, demographic changes, communication, interdisciplinarity, technology acceptance, user experience, emotional considerations, and provision of empirical metrics.
Human-centered design of work systems in the transition to industry 4.0 [6]	B. A. Kadir and O. Broberg	2021	Human-centered design achieves worker well-being and high system performance through a novel method combining human factors/ergonomics (HF/E), strategic design (BSC, objectives, targets), work system aspects (SOFT dimensions), and a seven-step approach for designing work systems.
Optimization of Productivity and Worker Well-Being by Using a Multi-Objective Optimization Framework [24]	A. I. Pascual, D. Högberg, D. Lämkull, E. P. Luque, A. Syberfeldt, and L. Hanson	2021	Current tools prioritize productivity and worker well-being but overlook anthropometric diversity; proactive workstations and multi-objective optimization of RULA scores, supported by digitalization, enable more accurate and individually tailored workplace solutions.
Leveraging Gamification in Industry 5.0: Tailored Solutions for Workplace’ Employees [25]	L. Cónego, R. Pinto, J. Pinto, and G. Gonçalves	2024	Integrating gamification elements into workplace practices enhances employee engagement and motivation to meet ergonomic standards, while leveraging modern technologies on mobile and web platforms ensures alignment with current business trends and needs.
Towards modern inclusive factories: A methodology for the development of smart adaptive human-machine interfaces [26]	V. Villani, L. Sabattini, J. N. Czerniaki, A. Mertens, B. Vogel-Heuser, and C. Fantuzzi	2017	Human-centered systems should measure user capabilities, adapt HMI information, and provide individualized interfaces and training models to create inclusive, flexible work environments tailored to age, education, cognitive and physical abilities, and task experience.
Improving Human Awareness During Collaboration with Robot: Review [27]	S. Grushko et al.	2021	In collaborative systems, user information must be intuitive, unobtrusive, and designed with consideration for individual user experience and security standards.
Development of Ergonomic User Interfaces for the Human Integration in Cyber-Physical Systems [28]	A. Cachada et al.	2019	The user interface is set to collect user feedback for data regarding effectiveness, efficiency, and satisfaction.
Camera 3D positioning mixed reality-based interface to improve worker safety, ergonomics and productivity [29]	A. Muñoz, A. Martí, X. Mahiques, L. Gracia, J. E. Solanes, and J. Tornero	2020	A mixed reality-based worker interface for industrial camera 3D positioning improves safety, productivity, and ergonomics through its intuitive and user-friendly design.
Predictive health a+B39:B45nalysis in industry 5.0: A scientometric and systematic review of Motion Capture in construction [30]	M. H. Rahman, M. R. Hasan, N. I. Chowdhury, M. A. B. Syed, and M. U. Farah	2024	The motion capture (MoCap) system enables the real-time analysis of worker movements, enhancing the understanding of ergonomic issues, personalizing training, and adapting the work environment to improve efficiency and safety.
Artificial Intelligence and its Impact on Leaders and Leadership [31]	Y. Peifer, T. Jeske, and S. Hille	2022	Integrating motion capture (MoCap) systems with AI enhances the analysis and optimization of work conditions but raises socio-technical and ethical challenges, requiring leaders to involve stakeholders, balance human–AI task allocation, and ensure criteria such as individual protection, trustworthiness, useful work sharing, and conducive working conditions emphasizing communication, cooperation, and social inclusion.
Ethical Personalisation and Control Systems for Smart Human-Centred Industry 5.0 Applications [32]	C. Murphy, P. J. Carew, and L. Stapleton	2022	Ethical challenges in human-centered 5.0 systems, including privacy, personal ethics, cultural ethics, and bias, necessitate an open communication policy to address data usage and storage transparently while treating user data with care and respect.
Enhancing Employee Green Performance through Green Training: The Mediating Influence of Organizational Green Culture and Work Ethic in the Mining Sector [33]	H. Sun, G. Mulindwa Bahizire, J. B. Bernard Pea-Assounga, and T. Chen	2024	Establishing ethical policies, fostering a supportive organizational culture, and training employees in ethics are critical for ensuring privacy and preventing information misuse, with recommendations for HR managers to develop consistent training programs, establish ethics committees, and regularly review and adapt ethical standards to address technological and societal challenges.
Smooth and Resilient Human–Machine Teamwork as an Industry 5.0 Design Challenge [34]	E. Kaasinen, A.-H. Anttila, P. Heikkilä, J. Laarni, H. Koskinen, and A. Väätänen	2022	Joint cognitive systems integrate ethical considerations and methods like actor–network theory, concept of operations, and ethically aware design to enhance human–machine collaboration, viewing humans and machines as equal actors, enabling dynamic role analysis, proactive ethical thinking, and sustainable design.
Defining the meaning of ‘sustainable work’ from activity-centered ergonomics and psychodynamics of Work’s perspectives [35]	C. M. Brunoro, I. Bolis, T. F. A. C. Sigahi, B. C. Kawasaki, and L. I. Sznelwar	2020	Sustainable work, rooted in activity-centered ergonomics and psychodynamics of work, emphasizes addressing root causes rather than just effects, integrating corporate sustainability guidelines, respecting human rights and labor laws, and fostering meaningful, pleasurable work that prioritizes health, well-being, and creativity within the organizational culture.

Table 3

Key findings—digital human modeling.

Title	Authors	Year Published	Key Findings
Developing a digital human modeling toolset: Simulating elderly posture in Grasshopper to optimize living environments [36]	H. Yuan	2024	Digital human modeling creates digital representations of the human body to simulate and analyze behavior, movements, and interactions across fields like ergonomics, biomechanics, healthcare, and design, enhancing system and environment evaluations.
Industry 4.0, Innovation and Design. A new approach for ergonomic analysis in manufacturing system [37]	E. Laudante	2017	Virtual ergonomics enables the preliminary assessment of posture-related issues, enhancing worker well-being and safety while facilitating new design experiences and scenario planning critical for manufacturing in the digital era.
Ergonomic Assessment Methods Enhanced by IoT and Simulation Tools [38]	M. Caterino, P. Manco, M. Rinaldi, R. Macchiaroli, and A. Lambiase	2021	A predictive model, combined with simulation-based ergonomic validation and standard methods like OWAS, NIOSH, OCRA, and EAWS, effectively evaluates biomechanical overload risks, leveraging digital human models for comprehensive assessment.
Application of wearable technology for the ergonomic risk assessment of healthcare professionals: A systematic literature review [39]	I. Sabino et al.	2024	JACK software, an advanced ergonomic simulation tool using 3D modeling, facilitates human movement simulation, ergonomic risk analysis, and working condition optimization, enhancing workplace safety and efficiency.
NexGen Ergonomics Inc. HumanCAD [40]	D. Pinchefsky	2019	HumanCAD is an ergonomic software that creates and analyzes digital human models in various work environments, utilizing anthropometric data, movement characteristics, and Microsoft Kinect for motion capture, with integrated tools for biomechanical modeling and ergonomic analyses using methods like NIOSH, RULA, and OWAS.
Digital twins to enhance the integration of ergonomics in the workplace design [17]	F. Caputo, A. Greco, M. Fera, and R. Macchiaroli	2019	Siemens Tecnomatix, equipped with the OWAS method, utilizes immersive reality tools and motion capture systems like cyber gloves to enhance simulation and data analysis capabilities.
A Virtual Reality Approach for Assisting Sustainable Human-Centered Ergonomic Design: The ErgoVR tool [41]	V. M. Manghisi, A. Evangelista, and A. E. Uva	2022	A virtual reality-based multiplayer tool using low-cost body tracking technology enables real-time and offline ergonomic risk assessments via RULA principles, offering immersive posture visualization for sustainable user-centered design; however, challenges include ambient lighting, wrist joint tracking limitations, and posture self-occlusions when using devices like Microsoft Kinect.
Human factors, ergonomics and Industry 4.0 in the Oil&Gas industry: a bibliometric analysis [42]	F. Longo, A. Padovano, L. Gazzaneo, J. Frangella, and R. Diaz	2021	Digital human simulation aids in analyzing ergonomics and working postures, providing valuable insights for designing active exoskeletons for arms and shoulders.
An assessment of the realism of digital human manikins used for simulation in ergonomics [43]	A. Nérot, W. Skalli, and X. Wang	2021	Accurate reproduction of external anthropometric dimensions and correct linkage of external body geometry to the internal skeleton are critical in digital human modeling to ensure realistic biomechanical analysis, prevent errors, and reduce the risk of worker injuries.

Table 4

Key findings—wearables and hardware.

Title	Authors	Year Published	Key Findings
Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics—An Overview of Current Applications, Challenges, and Future Opportunities [44]	C. M. Lind, F. Abtahi, and M. Forsman	2023	There is a growing need for more accurate and efficient workload assessment in the modern work environment.
A practical guide for selecting continuous monitoring wearable devices for community-dwelling adults [45]	J. K. Lu, W. Wang, J. Goh, and A. B. Maier	2024	Digital health technologies, including wearable devices, are valuable for monitoring lifestyle and health parameters, with their selection being influenced by measurement objectives, user characteristics, available resources, and the devices’ accuracy and integration capabilities.
A Preliminary Experimental Study on the Workers’ Workload Assessment to Design Industrial Products and Processes [46]	A. Brunzini, M. Peruzzini, F. Grandi, R. K. Khamaisi, and M. Pellicciari	2021	In modern work environments, non-invasive wearable devices monitoring human activity and physiological parameters, combined with subjective self-assessment questionnaires, enable the simultaneous analysis of physical and cognitive workload.
The impact of self-conscious emotions on the continuance intention of digital voice assistants in private and public contexts [47]	P. Kowalczuk and J. Musial	2024	Digital assistants like Siri, Google Assistant, and Amazon Alexa offer intuitive access to AI-processed knowledge and task management, enhancing user convenience, but they also raise ethical concerns related to their widespread use and integration.
Value-Oriented and Ethical Technology Engineering in Industry 5.0: A Human-Centric Perspective for the Design of the Factory of the Future [48]	F. Longo, A. Padovano, and S. Umbrello	2022	A value-sensitive design (VSD) approach illustrates how technologies enabling human–machine symbiosis in the smart factory can be designed to embody elicited human values.
Smart Interactive Technologies in the Human-Centric Factory 5.0: A Survey [49]	D. Brunetti, C. Gena, and F. Vernero	2022	The Internet of Things (IoT) architecture, combined with specialized software, enables real-time posture monitoring, self-learning, and tracking of work results, improving worker mobility and supporting new technologies while raising concerns over safety data and privacy.
Monitoring of shop-floor workers postural stability through the use of smart soles [50]	D. Teixeira, J. Ferreira, and R. Gonçalves	2022	Smart soles, developed as wearable devices, collect data on postural characteristics, contributing to improved ergonomics and worker well-being in production environments, particularly by supporting stable body positions over extended periods.
Application of wearable technology for the ergonomic risk assessment of healthcare professionals: A systematic literature review [39]	I. Sabino et al.	2024	Posture monitoring sensors like Lumo Lift and Upright Go provide real-time feedback to help users maintain proper ergonomic postures, preventing back pain and improving overall health.
Gyroscope vector magnitude: A proposed method for measuring angular velocities [51]	H. Chen, M. C. Schall, and N. B. Fethke [51]	2023	An innovative method using gyroscopes to measure the angular velocities of upper limb movement in all planes has been proposed to reduce the risk of musculoskeletal disorders caused by high movement speeds.
A human-centric system combining smartwatch and LiDAR data to assess the risk of musculoskeletal disorders and improve ergonomics of Industry 5.0 manufacturing workers [53]	F. Pistolesi, M. Baldassini, and B. Lazzerini	2024	An AI-based system using 2D LiDAR and smartwatches to track workers’ positions during standardized tasks achieved an impressive 98% tracking accuracy based on data collected from 30 participants performing six typical assembly tasks in accordance with ISO 11226 [52].
Exploring the potential of Operator 4.0 interface and monitoring [54]	M. Peruzzini, F. Grandi, and M. Pellicciari	2020	A monitoring system combining eye tracking, wearable biosensors, and ergonomic protocols enables the assessment of human–machine interaction, user experience, and workplace design, using physiological data (e.g., heart rate, breathing rate, pupil dilation) to create accurate digital twins for optimized workflows.
Human Interventions in the Smart Factory—A Case Study on Co-Designing Mobile and Wearable Monitoring Systems with Manufacturing Staff [55]	M. Baldauf, S. Müller, A. Seeliger, T. Küng, A. Michel, and W. Züllig	2021	Workers prefer smartwatches and smartphones over other tracking and assistance gadgets, with smartwatches being the most favored, although concerns about message clarity arise when multiple notifications are displayed; meanwhile, tablets are considered cumbersome to carry and should fit into pockets.
Ergonomic Assessment Methods Enhanced by IoT and Simulation Tools [38]	M. Caterino, P. Manco, M. Rinaldi, R. Macchiaroli, and A. Lambiase	2021	Worker suits equipped with IoT technology can collect data for ergonomic analysis, with the real-time evaluation of workstation design being facilitated by transferring data to simulation software.
Wearable Mapping Suit: Body Mapping for Identification Wearables [56]	F. Fröbel, M. Beuthel, and G. Joost	2021	A wearable mapping suit can be used to combine prototyping and body storming techniques and human-centered development to enable ideation for wearable technology directly on the potential user.
Self-contained optical-inertial motion capturing for assembly planning in digital factory [57]	W. Fang, L. Zheng, and J. Xu	2017	The motion capture method, combining optical and inertial sensors, offers real-time, portable, and low-cost ergonomic analysis by avoiding occlusions and installation issues, and it can be run on consumer mobile devices to monitor workers’ activity on the shop floor.
A methodological framework to integrate motion capture system and virtual reality for assembly system 4.0 workplace design [58]	M. Simonetto, S. Arena, and M. Peron	2022	Motion capture systems and virtual reality hold promising potential but must be developed to enhance productivity while adhering to occupational safety and health principles.
An evaluation for VR glasses system user experience: The influence factors of interactive operation and motion sickness [59]	M. Yu, R. Zhou, H. Wang, and W. Zhao	2019	Virtual reality (VR) can impact users’ health positively or negatively, with common issues including seasickness, visual problems, and physical injury; proper adjustment of glasses and the ergonomic environment, along with regular breaks, are necessary to mitigate these effects.
Human-centered knowledge graph-based design concept for collaborative manufacturing [60]	L. Nagy, T. Ruppert, and J. Abonyi	2022	To implement motion capture (MoCap) and VR systems, five steps are proposed: input collection, workplace design for well-being, data collection (including anthropometric data for digital twins), data analysis for productivity and ergonomics, and ergo-productivity satisfaction, with a software application helping to evaluate workplace ergonomics through metrics and augmented reality-based smart monitoring systems.
A Contribution to Workplace Ergonomics Evaluation Using Multimedia Tools and Virtual Reality [61]	R. Leskovský, E. Kučera, O. Haffner, J. Matišák, D. Rosinová, and E. Stark	2019	Using VR integrated with the Unity 3D engine offers an affordable solution for managing workplace environments and processes, providing alternative views of work processes, designing new environments, and training new employees.
Virtual reality simulation of human-robot coexistence for an aircraft final assembly line: process evaluation and ergonomics assessment [62]	K. Ottogalli, D. Rosquete, J. Rojo, A. Amundarain, J. María Rodríguez, and D. Borro	2021	VR can also be used to study the ergonomics of the human worker without compromising their safety. Motion data can be transmitted to the Perception Neuron v2 MoCap system, which allows for real-time ergonomics analysis. Ergonomics is then measured with the OWAS method.
Virtual reality relaxation for mental health staff in complex care services: A feasibility and acceptability study [63]	G. Williams, M. Riaz, E. Drini, and S. Riches	2024	Virtual reality can also be used in the workplace as a means of improving employee well-being, particularly for those who work in challenging environments and may experience high levels of stress and poor well-being.
F. Longo, L. Nicoletti, and A. Padovano [64]	F. Longo, L. Nicoletti, and A. Padovano	2017	AR can act as an intelligent tutoring system, providing real-time feedback and augmented content to operators, enhancing their skills and abilities to perceive and act in complex human–machine interactions, reducing accident risks, and supporting data-driven decision-making.
Augmented reality-assisted cloud additive manufacturing with digital twin technology for multi-stakeholder value Co-creation in product innovation [65]	S. Xu, Y. Lu, and C. Yu	2024	AR enhances communication and collaboration in the work environment by enabling real-time remote teamwork through shared virtual objects, and the AR-CAM framework integrates advanced technologies like digital twins, constructive manufacturing, and additive manufacturing to promote effective collaboration and value co-creation.
Bridging the Skills Gap of Workers in Industry 4.0 by Human Performance Augmentation Tools: Challenges and Roadmap [66]	V. D. Pasquale, V. De Simone, C. Franciosi, P. Morra, and S. Miranda	2017	AR aids operators in maintaining and repairing equipment by displaying instructions and schematics directly on the equipment, improving the efficiency of diagnosing problems and making repairs.

Table 5

Key findings—aging population.

Title	Authors	Year Published	Key Findings
A methodological framework to integrate motion capture system and virtual reality for assembly system 4.0 workplace design [58]	M. Simonetto, S. Arena, and M. Peron	2022	The aging workforce relates to reduced flexibility and strength and greater experience of older operators.
Industry 5.0: prioritizing human comfort and productivity through collaborative robots and dynamic task allocation [69]	I. Granata, M. Faccio, and G. Boschetti	2024	It is crucial to implement strategies that not only take into account changes in worker flexibility and strength but also optimize working conditions and leverage their extensive experience to maximize productivity and safety.
The sociodemographic challenge in human-centred production systems—a systematic literature review [70]	J. Alves, T. M. Lima, and P. D. Gaspar	2022	The aging workforce presents challenges in physical, cognitive, ergonomic, and well-being aspects in Industry 4.0 and 5.0 environments, but addressing these issues can improve production systems by reducing errors, increasing flexibility and performance, and enhancing human safety.
A framework to design a human-centred adaptive manufacturing system for aging workers [71]	M. Peruzzini and M. Pellicciari	2017	The aging population poses challenges for human-centered adaptive manufacturing systems due to reduced physical and cognitive abilities, required workplace adaptations, new assistive technologies, age-oriented production models, age-friendly workspaces integrating Industry 4.0 solutions, and updated criteria for human–robot interactions.
he three pillars of tomorrow: How Marketing 5.0 builds on Industry 5.0 and impacts Society 5.0? [72]	M. Bakator, D. Ćoćkalo, V. Makitan, S. Stanisavljev, and M. Nikolić	2024	Industry 5.0 offers solutions to the challenges of an aging workforce by integrating advanced robotics, AI, and collaborative technologies to compensate for physical limitations, harnessing experience and knowledge, and providing personalized assistive technologies and ergonomic work environments to sustain labor market participation.
The sociodemographic challenge in human-centred production systems—a systematic literature review [70]	J. Alves, T. M. Lima, and P. D. Gaspar	2022	Older workers are more prone to muscle fatigue and slower learning, making real-time monitoring essential to avoid critical issues while fostering “smart operators” and ensuring sustainability across four types: behavioral, mental, physical, and psychosocial, addressing safety, fatigue, load management, and human–robot interactions.

Table 6

Key findings—collaborative robotics and human–machine relations.

Title	Authors	Year Published	Key Findings
Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review [73]	L. Gualtieri, E. Rauch, and R. Vidoni	2021	Occupational health and safety in collaborative robotics focus on preventing human–robot collisions, addressing physical ergonomics through task scheduling and motion control, and incorporating cognitive ergonomics to reduce mental stress and psychological discomfort caused by unpredictable robot behavior.
Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey [74]	S. Proia, R. Carli, G. Cavone, and M. Dotoli	2022	Safety in collaborative robotics involves control algorithms to prevent human–robot collisions, limiting forces, torques, and speeds while balancing the benefits of physical task assistance with the potential for stress induction in workers.
Balancing and scheduling assembly lines with human-robot collaboration tasks [75]	A. Nourmohammadi, M. Fathi, and A. H. C. Ng	2022	Human–Robot collaboration enhances productivity and workers’ well-being through optimized task allocation, improved safety, and system flexibility, with the integration of technologies like augmented reality and collision detection occurring to support real-time operator interactions with collaborative robots.
Robust dynamic robot scheduling for collaborating with humans in manufacturing operations [76]	G. V. Tchane Djogdom, R. Meziane, and M. J.-D. Otis	2024	A planning approach combining offline proactive planning with online reactive adjustments enhances human–robot collaboration by balancing idle time and production efficiency, proving more effective than purely proactive or reactive strategies.
Human factors, ergonomics and Industry 4.0 in the Oil&Gas industry: a bibliometric analysis [42]	F. Longo, A. Padovano, L. Gazzaneo, J. Frangella, and R. Diaz	2021	5G technology is a necessity in human–robot collaboration systems, AR-assisted operations, and data-driven interaction with the digital twin.
Influence of human-machine interactions and task demand on automation selection and use [77]	J. Navarro, L. Heuveline, E. Avril, and J. Cegarra	2018	Including human–machine interaction in automatization provides better results than technology-centered models.
Human factors in cobot era: a review of modern production systems features [78]	M. Faccio et al.	2023	Collaborative systems are more complex than traditional ones as interaction leads to some advantages and disadvantages. How the operator feels about the installed work cell influences the overall performance of such industrial application.
n evaluation methodology for the conversion of manual assembly systems into human-robot collaborative workcells [79]	L. Gualtieri, E. Rauch, R. Vidoni, and D. T. Matt	2019	Multicriteria decision support methods using the HRAA algorithm help SMEs evaluate the conversion of manual assembly workstations into collaborative human–robot work cells by assessing technical, safety, ergonomic, qualitative, and economic feasibility through four hierarchical evaluation indexes.
A framework for safe and intuitive human-robot interaction for assistant robotics [80]	P. D. Cen Cheng, F. Sibona, and M. Indri	2022	Individual designs of collaborative workplaces enable better output in terms of both the productivity and efficiency of workers.
Human-cobot collaboration’s impact on success, time completion, errors, workload, gestures and acceptability during an assembly task [81]	É. Fournier, C. Jeoffrion, B. Hmedan, D. Pellier, H. Fiorino, and A. Landry	2024	Job quality in human–robot collaboration is influenced by cognitive workload, collaboration fluency, trust, acceptance, and satisfaction, with predictable robots reducing cognitive overload, improving task quality, and enhancing trust and enjoyment, especially when operators are aware of robots’ speed and motion trajectories.
What about the Human in Human Robot Collaboration? A literature review on HRC’s effects on aspects of job quality [82]	S. Baltrusch, F. Krause, A. de Vries, W. Dijk, and M. P. Looze	2021	Prior notifications of robot parameters and repetitive behavior enhance productivity, prevent mistakes, and improve situation awareness while fostering trust, better teamwork, and proactive human–robot collaboration through bi-directional empathy and a holistic understanding.
How automation level influences moral decisions of humans collaborating with industrial robots in different scenarios [83]	A. Eich, A. Klichowicz, and F. Bocklisch	2023	Human–Robot collaboration varies across four spatial levels, and the perceived proximity between humans and robots influences decision-making, with closer distances leading to more utilitarian decisions; the cognitive system maturity of the robot can also affect moral decision-making in context-specific situations.
On the role of human operators in the design process of cobotic systems [84]	M. Bounouar, R. Bearee, A. Siadat, and T.-H. Benchekroun	2022	The design of robots should prioritize ease of control, usability, and quick learning for operators, incorporating user-centered design principles while considering economic viability, operator participation, acceptability, safety, and technical feasibility.
Lean Manufacturing and Ergonomics Integration: Defining Productivity and Wellbeing Indicators in a Human–Robot Workstation [85]	A. Colim et al.	2021	Robotic workstations can be assessed using methods like Rapid Upper Limb Assessment, Revised Strain Index, and well-being questionnaires, with a human-centered lean approach proving effective in improving collaborative workstations and receiving positive worker perceptions regarding ergonomic aspects.
Assembly Line Balancing with Collaborative Robots under consideration of Ergonomics: a cost-oriented approach [86]	C. Weckenborg and T. S. Spengler	2019	The use of collaborative robots can reduce ergonomic load on workers, particularly in demanding environments that require a higher degree of automation.
Safety 4.0 Approach for Collaborative Robotics in the Factories of the Future [87]	L. Caruana and E. Francalanza	2023	Novel communication levels between humans and machines again raise certain security concerns, while the intense data exchange increases the risk of cyberattacks.
Sustainable Human–Machine Collaborations in Digital Transformation Technologies Adoption: A Comparative Case Study of Japan and Germany [88]	Y. W. Park and J. Shintaku	2022	Cultural differences influence the implementation of collaborative robots, with the US and Germany focusing on autonomous control through IoT, while Japan emphasizes robot-human collaboration, reflecting a philosophy where robots complement multi-skilled workers who gain proficiency through repeated tasks.
Supporting decision-making of collaborative robot (cobot) adoption: The development of a framework [89]	A. Silva, A. Correia Simões, and R. Blanc	2024	The implementation of collaborative robots (cobots) in manufacturing addresses the fifth industrial revolution’s need for customized mass production, with a proposed decision framework combining quantitative and qualitative criteria to guide sustainable integration based on a weighted scoring method adaptable to specific enterprise needs.
A tool to evaluate industrial cobot safety readiness from a system-wide perspective: An empirical validation [90]	N. Berx, W. Decré, and L. Pintelon	2024	The CSRAT tool, designed to assess the safety readiness of collaborative robots (cobots) across five dimensions and 23 risk factors, was validated through a web-based survey and focus groups, confirming its effectiveness in preparing for cobot installation and identifying associated risks.

Table 7

Key findings—human factors and ergonomics.

Title	Authors	Year Published	Key Findings
Human factors and ergonomics in the operating room [91]	P. Webster Kristen L. W. and M. Haut PhD, FACS, Elliott R.	2024	Human factors and ergonomics (HF/E) is an interdisciplinary field that aims to optimize interactions between people, technology, and the work environment to enhance efficiency, safety, and comfort while reducing errors, stress, and fatigue, applied across sectors like industry, healthcare, transportation, and information technology.
Advanced Industrial Tools of Ergonomics Based on Industry 4.0 Concept [92]	M. Gašová, M. Gašo, and A. Štefánik	2017	Human-centric systems emphasize the synergy between micro- and macro-ergonomics, incorporating assistive technologies like exoskeletons and smart gesture control to improve worker health, safety, and efficiency while understanding cognitive, physical, and psychosocial aspects and utilizing mobile apps as quick risk assessment tools to create healthy work conditions.
Human factors and ergonomics in manufacturing in the industry 4.0 context—A scoping review [93]	A. Reiman, J. Kaivo-oja, E. Parviainen, E.-P. Takala, and T. Lauraeus	2021	Companies must recognize the maturity levels of both technical and human factors and ergonomics (HF/E) to avoid pitfalls during strategy implementation, as imbalances in technological and HF/E maturity can lead to non-optimal technology utilization, decreased productivity, health and safety hazards, and challenges in personnel motivation and commitment to work, highlighting the need for a holistic approach that integrates micro- and macro-ergonomics in manufacturing.
Fatigue, personnel scheduling and operations: Review and research opportunities [94]	S. Xu and N. G. Hall	2021	Effective algorithms and heuristic solutions for scheduling can mitigate fatigue by incorporating variables such as rest break placement and shift length, reducing work-related fatigue while optimizing worker planning and machine performance.
Industry 4.0 and the human factor—A systems framework and analysis methodology for successful development [95]	W. P. Neumann, S. Winkelhaus, E. H. Grosse, and C. H. Glock	2021	Indirect costs related to employees’ musculoskeletal disorders (MSDs) in manufacturing, such as hiring, training, and reduced performance, can be mitigated by applying human factors, leading to improvements in productivity, technology implementation, quality, and system reliability.
Participatory Ergonomics in the context of Industry 4.0: a literature review [96]	E. E. Broday	2021	Participatory ergonomics involves workers in ergonomic analysis and design, helps prevent musculoskeletal disorders and injuries, reduces production time and costs, and can provide a competitive advantage through the use of new technologies.
Development of metric method and framework model of integrated complexity evaluations of production process for ergonomics workstations [97]	F. Kong	2019	Integrated complexity evaluation methods of production processes, considering physical and cognitive load, assess factors like operation difficulty, information processing, and time stress, providing valuable data for task assignment, operator selection, training, work organization, and performance prediction.
Task Allocation in Human-Robot Collaboration: A Simulation-based approach to optimize Operator’s Productivity and Ergonomics [98]	A. Baratta, A. Cimino, F. Longo, G. Mirabelli, and L. Nicoletti	2024	A study using a fatigue model with exponential and logarithmic functions in a human–robot collaborative environment showed that robot support and F-WS systems improve operator speed, efficiency, and reduce fatigue, emphasizing the importance of integrating human–robot collaboration, intelligent task allocation, and ergonomics for sustainable development.
Human-technology integration in smart manufacturing and logistics: current trends and future research directions [99]	C. Cimini, A. Lagorio, S. Cavalieri, O. Riedel, C. E. Pereira, and J. Wang	2022	Implementing lean management without considering human factors and ergonomics can negatively impact workers’ quality of work life and performance; however, soft lean practices that incorporate HF/E principles can enhance sustainability, with psychosocial and physical factors having a greater influence on lean performance than cognitive factors. Additionally, the complexity of tasks and the involvement of HF/E make it harder to substitute technology for the operator.
Smart Palletisation: Cognitive Ergonomics in Augmented Reality Based Palletising [82]	V. Kretschmer, T. Plewan, G. Rinkenauer, and B. Maettig	2018	Job rotation can improve physical factors like reducing MSDs but may negatively impact psychosocial factors such as job satisfaction and workers’ intention to stay, while job autonomy enhances flexibility, decision-making, and task method selection, contributing to greater job satisfaction and work experience.
Smart Palletisation: Cognitive Ergonomics in Augmented Reality Based Palletising [100]	C. Cimini, A. Lagorio, S. Cavalieri, O. Riedel, C. E. Pereira, and J. Wang	2018	Cognitive factors such as situation awareness, human reliability (human error) and decision-making skills are positively related to operational performance.
An Online Framework for Cognitive Load Assessment in Industrial Tasks [101]	M. Lagomarsino, M. Lorenzini, E. De Momi, and A. Ajoudani	2022	Physical ergonomics has more influence on the enhancement of lean performance than organizational ergonomics. Cognitive ergonomics have the least influence on lean performance enhancement.
An empirical investigation on association between human factors, ergonomics and lean manufacturing [103]	T. Sakthi Nagaraj and R. Jeyapaul	2021	Lean management primarily affects workers’ psychosocial factors, but cognitive ergonomics positively influences lean performance by enhancing workers’ situation awareness, reliability, and decision-making skills, which can be improved through targeted ergonomic interventions.
Applications and future perspectives of integrating Lean Six Sigma and Ergonomics [104]	I. Vicente, R. Godina, and A. Teresa Gabriel	2024	The integration of Lean Six Sigma (LSS) methodology with ergonomics enables continuous improvement in organizations by achieving improvement goals without compromising employee safety and health, enhancing quality, efficiency, productivity, and addressing ergonomic issues for sustainable performance improvement.
Application of Innovative Tools to Design Ergonomic Control Dashboards [105]	F. Grandi, M. Peruzzini, C. Campanella, and M. Pellicciari	2022	Human factors like physical fatigue, attention, mental workload, stress, trust, and emotional state significantly impact physiological responses, such as changes in heart rate, muscle tension, and facial expressions, and wearable technologies, which provides an integrated approach for measuring these human factors.
Collaboration Between Humans and Robots in Organizations: A Macroergonomic, Emotional, and Spiritual Approach [106]	V. Firescu, M.-L. Gaşpar, I. Crucianu, and E. Rotariu	2023	The HCV model, developed for human-centric systems, provides a sociotechnical perspective on human–machine interaction, predicting employee reactions and guiding organizational strategy by integrating human identity, technology, and organizational factors along with elements like social belonging, working conditions, and external environmental readiness.
Cybergonomics: Proposing and justification of a new name for the ergonomics of Industry 4.0 technologies [107]	M. Pouyakian	2022	Cyberergonomics, a term proposed for addressing ergonomic aspects of cyberspace in Industry 5.0, focuses on optimizing safety, productivity, and health in digital work environments, considering issues like psychological distraction, privacy, and cyberattacks, and it is particularly useful for addressing different work approaches across age groups.
A Social Design Approach: Enhancement of Local Social Dialogue on the Transformation of Work by Digital Technology [108]	L. Galey, V. Terquem, and F. Barcellini	2022	The motion analysis system integrates motion capture (MoCap) technology and specialized software for ergonomic analysis (e.g., OWAS, REBA, NIOSH, EAWS), providing output data such as time and space analysis, hand displacement, velocity trends, cumulative vertical movements, and control volume analysis to differentiate value-added and non-value-added activities.
Ergonomic Evaluation of Body Postures in Order Picking Systems Using Motion Capturing [109]	F. Feldmann, R. Seitz, V. Kretschmer, N. Bednorz, and M. T. Hompel	2019	A MoCap system can be used to digitalize the ergonomics analysis tool Key Indicator Method (KIM), which can cover only one part of the ergonomics assessment, which is body posture, without including the time and load weighting.
Technology Acceptance and Leadership 4.0: A Quali-Quantitative Study [110]	M. Molino, C. G. Cortese, and C. Ghislieri	2021	Supervisor support, role clarity, and effective communication methods (e.g., videos, posters) are crucial for employee well-being, while involving workers in workshops and training on technological transformation, especially senior workers, can help manage technological changes and overcome the age gap.
In pursuit of humanised order picking planning: methodological review, literature classification and input from practice [111]	T. De Lombaert, K. Braekers, R. De Koster, and K. Ramaekers	2023	Awareness of individual worker preferences can enhance motivation and performance, with Industry 5.0 planning models offering personalized user interfaces to enable tailored decisions, predict pleasant working conditions, and incorporate well-designed breaks and job rotation to prevent monotony.
Missing focus on Human Factors—organizational and cognitive ergonomics—in the safety management for the petroleum industry [112]	S. O. Johnsen, S. S. Kilskar, and K. R. Fossum	2017	The lack of human factors integration in the design process impacts worker safety and resilience, with a need for early validation and regulatory checks; addressing this gap requires focusing on management thinking, media, and education to strengthen the human factors framework.

Table 8

Key findings—occupational health and safety.

Title	Authors	Year Published	Key Findings
Exploring the synergies between collaborative robotics, digital twins, augmentation, and industry 5.0 for smart manufacturing: A state-of-the-art review [113]	M. H. Zafar, E. F. Langås, and F. Sanfilippo	2024	In Industry 5.0, occupational safety and health (OSH) becomes more complex due to the close interdependence between people and technology, requiring new approaches to address both traditional risks and those emerging from human–machine collaboration with robots and autonomous systems.
Can Complexity-Thinking Methods Contribute to Improving Occupational Safety in Industry 4.0? A Review of Safety Analysis Methods and Their Concepts [114]	A. Adriaensen, W. Decré, and L. Pintelon	2019	Complex methods for health and safety risk assessment are needed as traditional OHS methods fail to analyze socio-technical issues collectively and identify emergent system properties; complexity-thinking methods enable a holistic analysis by focusing on joint problem-solving ensembles rather than decomposing humans, machines, and interfaces into separate units.
Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor [115]	C. I. Nwakanma, F. B. Islam, M. P. Maharani, J.-M. Lee, and D.-S. Kim	2021	Proactive activity monitoring enhances worker safety by detecting falling objects, human movement, and abnormal vibrations, especially in isolated work environments, with support coming from Internet of Things and Machine Learning to improve detection accuracy.
Designing interaction interface for supportive human-robot collaboration: A co-creation study involving factory employees [116]	H.-L. Cao et al.	2024	As machines and systems become more interconnected, protecting data and systems from cyber threats is crucial to prevent unauthorized interference that could compromise worker safety.

Table 9

Key findings—well-being.

Title	Authors	Year Published	Key Findings
Exploring Human-Centricity in Industry 5.0: Empirical Insights from a Social Media Discourse [117]	A. Padovano, M. Cardamone, M. Woschank, and C. Pacher	2024	Industry 5.0 emphasizes technological innovation and automation while prioritizing the well-being of employees, focusing on personalized work environments, work–life balance, and meaningful work that supports physical, mental health, personal development, and long-term company success.
Industrial metaverse towards Industry 5.0: Connotation, architecture, enablers, and challenges [118]	J. Guo et al.	2024	Industry 5.0 fosters greater collaboration between humans and machines, emphasizing inclusiveness and diversity to create work teams with varied perspectives and skills, boosting innovation, productivity, and quality while enhancing employee well-being, engagement, creativity, and satisfaction.
Is Industry 5.0 a Human-Centred Approach? A Systematic Review [119]	J. Alves, T. Lima, and P. Gaspar	2023	Well-being and motivating work environments are prioritized in Industry 5.0, with human-centricity being achieved by involving all stakeholders in the design and innovation processes while shifting from technological to socio-technological systems that demand continuous worker skill upgrades, defining Operator 5.0 as a resilient, self-evolving operator focused on system resilience.
Assessing ergonomics and biomechanical risk in manual handling of loads through a wearable system [120]	I. Conforti, I. Mileti, Z. Del Prete, and E. Palermo	2019	A transdisciplinary approach to human well-being in manufacturing, supported by Internet of Things (IoT), promotes a human-centered system, improving health, satisfaction, and performance by measuring physical, cognitive, and environmental aspects while evaluating six macro-categories of risk factors to enhance worker well-being and company performance.
How to improve worker’s well-being and company performance: a method to identify effective corrective actions [121]	M. Scafà, A. Papetti, A. Brunzini, and M. Germani	2019	Physical workplace optimization includes training sessions to enhance risk awareness, improve skills, and reduce cognitive effort during task execution, with several key performance indicators (KPIs) being identified to measure the improvements.
An automatic procedure based on virtual ergonomic analysis to promote human-centric manufacturing [122]	G. Fabio, P. Margherita, Z. Luca, and P. Marcello	2019	The conflict between operational performance and employee well-being, caused by a lack of standards, hinders the adoption of a human-centric approach; the use of digital tools for ergonomic analysis during process design helps define structured procedures for automatic data extraction and preventive workstation assessment using virtual analysis and the EAWS model.
Human-centered design of work systems in the transition to industry 4.0 [6]	B. A. Kadir and O. Broberg	2020	Active participation in decision-making enhances work motivation and pleasure, while the adoption of new technologies can initially cause frustration and division; however, once fully implemented, digital solutions improve worker well-being and physical and cognitive ergonomics, with well-being fluctuating during the transformational phase, decreasing due to the fear of change but improving after full adoption.

Table 10

Enables and barriers of human factors and ergonomics in Industry 5.0.

Enablers	Barriers:
Holistic Ergonomic Integration: Combining physical, cognitive, and organizational ergonomics enhances safety, efficiency, and comfort while minimizing stress and errors. Advanced tools like wearable devices, motion capture systems, and cyberergonomics improve workplace adaptability and foster a human-centered approach.	Inadequate Integration of Ergonomics in Industry 5.0: Traditional ergonomic methods fail to address the socio-technical complexities and emergent properties of human–machine collaboration in Industry 5.0.
Participatory Ergonomics: Involving workers in ergonomic design and system development enhances engagement, satisfaction, and alignment with individual needs, fostering creativity and productivity.	Insufficient Personalization and Inclusivity: Despite the emphasis on personalization, practical frameworks for tailoring systems to diverse employee demographics, including aging workers and those with cognitive or physical impairments, remain underdeveloped.
Assistive and Collaborative Technologies: Tools like collaborative robots (cobots), exoskeletons, AR/VR, and IoT-based frameworks provide real-time monitoring and ergonomic support, reducing physical strain and improving decision-making.	Conflict Between Performance and Well-Being: Balancing operational performance with employee well-being is hindered by the lack of standardized procedures and metrics, creating barriers to adopting human-centric approaches.
Lifelong Learning and Skill Development: Training programs focused on emotional intelligence, cognitive flexibility, and technology adaptation empower workers to collaborate effectively with advanced systems, enhancing well-being and operational performance.	Ethical and Privacy Concerns: Technologies like wearable sensors, AR/VR, and biometric tracking raise ethical issues around data ownership, consent, and potential misuse of sensitive worker information.
Resilience and Inclusivity: Industry 5.0 emphasizes diverse and inclusive workplaces, leveraging the unique contributions of employees across age groups and abilities to optimize productivity and innovation.	Limited Exploration of Cognitive and Psychosocial Ergonomics: Current metrics and assessments focus heavily on physical ergonomics, neglecting cognitive and psychosocial factors such as stress, trust, and cognitive workload.
Proactive Monitoring and Safety Measures: IoT and machine learning enable real-time detection of hazards such as falling objects and abnormal vibrations, enhancing occupational safety and health (OSH).	Barriers to Worker Engagement: Fear of change, lack of non-technical skills, and inadequate training during the adoption of new technologies lead to resistance and negatively affect well-being.
Digital Twin and Ergonomic Assessment Tools: Integration of tools like JACK, HumanCAD, and Siemens Tecnomatix facilitates detailed risk assessments and predictive modeling, improving ergonomic and task optimization.	High Costs and Complexity: The implementation of advanced technologies and ergonomic tools often requires specialized expertise and substantial investment, limiting accessibility, especially for small and medium-sized enterprises (SMEs).
	Cybersecurity Risks. The interconnected nature of Industry 5.0 systems amplifies cyber threats, posing risks to both worker safety and system integrity.
	Lack of Unified Ergonomic Standards. The absence of globally harmonized ergonomic frameworks leads to inconsistencies in designing and implementing human-centric systems.

References

1. Ordieres-Meré, J.; Gutierrez, M.; Villalba-Díez, J. Toward the industry 5.0 paradigm: Increasing value creation through the robust integration of humans and machines. Comput. Ind.; 2023; 150, 103947. [DOI: https://dx.doi.org/10.1016/j.compind.2023.103947]

2. Urbaniec, K.; Mikulčić, H.; Rosen, M.A.; Duić, N. A holistic approach to sustainable development of energy, water and environment systems. J. Clean. Prod.; 2017; 155, pp. 1-11. [DOI: https://dx.doi.org/10.1016/j.jclepro.2017.01.119]

3. Rannertshauser, P.; Kessler, M.; Arlinghaus, J.C. Human-centricity in the design of production planning and control systems: A first approach towards Industry 5.0. IFAC-PapersOnLine; 2022; 55, pp. 2641-2646. [DOI: https://dx.doi.org/10.1016/j.ifacol.2022.10.108]

4. Trstenjak, M.; Hegedić, M.; Cajner, H.; Opetuk, T.; Tošanović, N. Cognitive Ergonomics in Industry 5.0. Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems; Silva, F.J.G.; Ferreira, L.P.; Sá, J.C.; Pereira, M.T.; Pinto, C.M.A. Lecture Notes in Mechanical Engineering; Springer Nature Switzerland: Cham, Switzerland, 2024; pp. 763-770. [DOI: https://dx.doi.org/10.1007/978-3-031-38165-2_88]

5. Gualtieri, L.; Palomba, I.; Merati, F.A.; Rauch, E.; Vidoni, R. Design of Human-Centered Collaborative Assembly Workstations for the Improvement of Operators’ Physical Ergonomics and Production Efficiency: A Case Study. Sustainability; 2020; 12, 3606. [DOI: https://dx.doi.org/10.3390/su12093606]

6. Kadir, B.A.; Broberg, O. Human-centered design of work systems in the transition to industry 4.0. Appl. Ergon.; 2021; 92, 103334. [DOI: https://dx.doi.org/10.1016/j.apergo.2020.103334] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33264676]

7. Tugwell, P.; Tovey, D. PRISMA 2020. J. Clin. Epidemiol.; 2021; 134, pp. A5-A6. [DOI: https://dx.doi.org/10.1016/j.jclinepi.2021.04.008]

8. Zizic, M.C.; Mladineo, M.; Gjeldum, N.; Celent, L. From Industry 4.0 towards Industry 5.0: A Review and Analysis of Paradigm Shift for the People, Organization and Technology. Energies; 2022; 15, 5221. [DOI: https://dx.doi.org/10.3390/en15145221]

9. Yang, J.; Liu, Y.; Morgan, P.L. Human-machine interaction towards Industry 5.0: Human-centric smart manufacturing. Digit. Eng.; 2024; 2, 100013. [DOI: https://dx.doi.org/10.1016/j.dte.2024.100013]

10. Bechinie, C.; Zafari, S.; Kroeninger, L.; Puthenkalam, J.; Tscheligi, M. Toward human-centered intelligent assistance system in manufacturing: Challenges and potentials for operator 5.0. Procedia Comput. Sci.; 2024; 232, pp. 1584-1596. [DOI: https://dx.doi.org/10.1016/j.procs.2024.01.156]

11. Hattinger, M.; Stylidis, K. Transforming Quality 4.0 towards Resilient Operator 5.0 needs. Procedia CIRP; 2023; 120, pp. 1600-1605. [DOI: https://dx.doi.org/10.1016/j.procir.2023.12.002]

12. Gladysz, B.; Tran, T.; Romero, D.; van Erp, T.; Abonyi, J.; Ruppert, T. Current development on the Operator 4.0 and transition towards the Operator 5.0: A systematic literature review in light of Industry 5.0. J. Manuf. Syst.; 2023; 70, pp. 160-185. [DOI: https://dx.doi.org/10.1016/j.jmsy.2023.07.008]

13. Piccarozzi, M.; Silvestri, L.; Silvestri, C.; Ruggieri, A. Roadmap to Industry 5.0: Enabling technologies, challenges, and opportunities towards a holistic definition in management studies. Technol. Forecast. Soc. Change; 2024; 205, 123467. [DOI: https://dx.doi.org/10.1016/j.techfore.2024.123467]

14. Mourtzis, D.; Angelopoulos, J.; Panopoulos, N. A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0. Energies; 2022; 15, 6276. [DOI: https://dx.doi.org/10.3390/en15176276]

15. Fabio, G.; Giuditta, C.; Margherita, P.; Raffaeli, R. A human-centric methodology for the co-evolution of operators’ skills, digital tools and user interfaces to support the Operator 4.0. Robot. Comput.-Integr. Manuf.; 2025; 91, 102854. [DOI: https://dx.doi.org/10.1016/j.rcim.2024.102854]

16. Borchardt, M.; Pereira, G.M.; Milan, G.S.; Scavarda, A.R.; Nogueira, E.O.; Poltosi, L.C. Industry 5.0 Beyond Technology: An Analysis Through the Lens of Business and Operations Management Literature. Organizacija; 2022; 55, pp. 305-321. [DOI: https://dx.doi.org/10.2478/orga-2022-0020]

17. Caputo, F.; Greco, A.; Fera, M.; Macchiaroli, R. Digital twins to enhance the integration of ergonomics in the workplace design. Int. J. Ind. Ergon.; 2019; 71, pp. 20-31. [DOI: https://dx.doi.org/10.1016/j.ergon.2019.02.001]

18. Orso, V.; Ziviani, R.; Bacchiega, G.; Bondani, G.; Spagnolli, A.; Gamberini, L. Employee-centric innovation: Integrating participatory design and video-analysis to foster the transition to Industry 5.0. Comput. Ind. Eng.; 2022; 173, 108661. [DOI: https://dx.doi.org/10.1016/j.cie.2022.108661]

19. Wang, F.-K.; Rahardjo, B.; Rovira, P.R. Lean Six Sigma with Value Stream Mapping in Industry 4.0 for Human-Centered Workstation Design. Sustainability; 2022; 14, 17. [DOI: https://dx.doi.org/10.3390/su141711020]

20. Firu, A.; Tapirdea, A.; Chivu, O.; Feier, A.I.; Draghici, G. The Competences Required by the New Technologies in Industry 4.0 and the Development of Employees’ Skills. Acta Tech. Napoc. Ser. Appl. Math. Mech. Eng.; 2021; 64, pp. 109-116.

21. Sgarbossa, F.; Grosse, E.H.; Neumann, W.P.; Battini, D.; Glock, C.H. Human factors in production and logistics systems of the future. Annu. Rev. Control; 2020; 49, pp. 295-305. [DOI: https://dx.doi.org/10.1016/j.arcontrol.2020.04.007]

22. Abdous, M.-A.; Delorme, X.; Battini, D.; Berger-Douce, S. Multi-objective collaborative assembly line design problem with the optimisation of ergonomics and economics. Int. J. Prod. Res.; 2022; 61, pp. 7830-7845. [DOI: https://dx.doi.org/10.1080/00207543.2022.2153185]

23. Brauner, P.; Zie, M. Chapter 14 Human Factors in Production Systems Motives, Methods and Beyond. 2014; Available online: https://www.semanticscholar.org/paper/Chapter-14-Human-Factors-in-Production-Systems-%2C-Brauner-Zie/1694030ded36059ea28e7f0696b3dda9de11b8bf (accessed on 5 September 2023).

24. Pascual, A.I.; Högberg, D.; Lämkull, D.; Luque, E.P.; Syberfeldt, A.; Hanson, L. Optimization of Productivity and Worker Well-Being by Using a Multi-Objective Optimization Framework. IISE Trans. Occup. Ergon. Hum. Factors; 2021; 9, pp. 143-153. [DOI: https://dx.doi.org/10.1080/24725838.2021.1997834] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34724884]

25. Cónego, L.; Pinto, R.; Pinto, J.; Gonçalves, G. Leveraging Gamification in Industry 5.0: Tailored Solutions for Workplace’ Employees. Procedia Comput. Sci.; 2024; 232, pp. 1769-1778. [DOI: https://dx.doi.org/10.1016/j.procs.2024.01.175]

26. Villani, V.; Sabattini, L.; Czerniaki, J.N.; Mertens, A.; Vogel-Heuser, B.; Fantuzzi, C. Towards modern inclusive factories: A methodology for the development of smart adaptive human-machine interfaces. Proceedings of the 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA); Limassol, Cyprus, 13–15 September 2017; pp. 1-7. [DOI: https://dx.doi.org/10.1109/ETFA.2017.8247634]

27. Grushko, S.; Vysocky, A.; Suder, J.; Glogar, L.; Bobovsky, Z. IMPROVING HUMAN AWARENESS DURING COLLABORATION WITH ROBOT: REVIEW. MM Sci. J.; 2021; 2021, 5475. [DOI: https://dx.doi.org/10.17973/MMSJ.2021_12_2021181]

28. Cachada, A.; Barbosa, J.; Leitao, P.; Deusdado, L.; Costa, J.; Teixeira, J.; Teixeira, C.; Romero, L.; Moreira, P.M. Development of Ergonomic User Interfaces for the Human Integration in Cyber-Physical Systems. Proceedings of the 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE); Vancouver, BC, Canada, 12–14 June 2019; pp. 1632-1637. [DOI: https://dx.doi.org/10.1109/ISIE.2019.8781101]

29. Muñoz, A.; Martí, A.; Mahiques, X.; Gracia, L.; Solanes, J.E.; Tornero, J. Camera 3D positioning mixed reality-based interface to improve worker safety, ergonomics and productivity. CIRP J. Manuf. Sci. Technol.; 2020; 28, pp. 24-37. [DOI: https://dx.doi.org/10.1016/j.cirpj.2020.01.004]

30. Rahman, M.H.; Hasan, M.R.; Chowdhury, N.I.; Syed, M.A.B.; Farah, M.U. Predictive health analysis in industry 5.0: A scientometric and systematic review of Motion Capture in construction. Digit. Eng.; 2024; 1, 100002. [DOI: https://dx.doi.org/10.1016/j.dte.2024.100002]

31. Peifer, Y.; Jeske, T.; Hille, S. Artificial Intelligence and its Impact on Leaders and Leadership. Procedia Comput. Sci.; 2022; 200, pp. 1024-1030. [DOI: https://dx.doi.org/10.1016/j.procs.2022.01.301]

32. Murphy, C.; Carew, P.J.; Stapleton, L. Ethical Personalisation and Control Systems for Smart Human-Centred Industry 5.0 Applications. IFAC-PapersOnLine; 2022; 55, pp. 24-29. [DOI: https://dx.doi.org/10.1016/j.ifacol.2022.12.005]

33. Sun, H.; Bahizire, G.M.; Pea-Assounga, J.B.B.; Chen, T. Enhancing Employee Green Performance through Green Training: The Mediating Influence of Organizational Green Culture and Work Ethic in the Mining Sector. J. Clean. Prod.; 2024; 449, 141105. [DOI: https://dx.doi.org/10.1016/j.jclepro.2024.141105]

34. Kaasinen, E.; Anttila, A.-H.; Heikkilä, P.; Laarni, J.; Koskinen, H.; Väätänen, A. Smooth and Resilient Human–Machine Teamwork as an Industry 5.0 Design Challenge. Sustainability; 2022; 14, 2773. [DOI: https://dx.doi.org/10.3390/su14052773]

35. Brunoro, C.M.; Bolis, I.; Sigahi, T.F.A.C.; Kawasaki, B.C.; Sznelwar, L.I. Defining the meaning of ‘sustainable work’ from activity-centered ergonomics and psychodynamics of Work’s perspectives. Appl. Ergon.; 2020; 89, 103209. [DOI: https://dx.doi.org/10.1016/j.apergo.2020.103209]

36. Yuan, H. Developing a digital human modeling toolset: Simulating elderly posture in Grasshopper to optimize living environments. J. Build. Eng.; 2024; 90, 109308. [DOI: https://dx.doi.org/10.1016/j.jobe.2024.109308]

37. Laudante, E. Industry 4.0, Innovation and Design. A new approach for ergonomic analysis in manufacturing system. Des. J.; 2017; 20, pp. S2724-S2734. [DOI: https://dx.doi.org/10.1080/14606925.2017.1352784]

38. Caterino, M.; Manco, P.; Rinaldi, M.; Macchiaroli, R.; Lambiase, A. Ergonomic Assessment Methods Enhanced by IoT and Simulation Tools. Macromol. Symp.; 2021; 396, 2000310. [DOI: https://dx.doi.org/10.1002/masy.202000310]

39. Sabino, I.; Fernandes, M.D.C.; Cepeda, C.; Quaresma, C.; Gamboa, H.; Nunes, I.L.; Gabriel, A.T. Application of wearable technology for the ergonomic risk assessment of healthcare professionals: A systematic literature review. Int. J. Ind. Ergon.; 2024; 100, 103570. [DOI: https://dx.doi.org/10.1016/j.ergon.2024.103570]

40. Pinchefsky, D. NexGen Ergonomics Inc. HumanCAD ***. DHM and Posturography; Scataglini, S.; Paul, G. Academic Press: Cambridge, MA, USA, 2019; pp. 79-83. [DOI: https://dx.doi.org/10.1016/B978-0-12-816713-7.00007-6]

41. Manghisi, V.M.; Evangelista, A.; Uva, A.E. A Virtual Reality Approach for Assisting Sustainable Human-Centered Ergonomic Design: The ErgoVR tool. Procedia Comput. Sci.; 2022; 200, pp. 1338-1346. [DOI: https://dx.doi.org/10.1016/j.procs.2022.01.335]

42. Longo, F.; Padovano, A.; Gazzaneo, L.; Frangella, J.; Diaz, R. Human factors, ergonomics and Industry 4.0 in the Oil&Gas industry: A bibliometric analysis. Procedia Comput. Sci.; 2021; 180, pp. 1049-1058. [DOI: https://dx.doi.org/10.1016/j.procs.2021.01.350]

43. Nérot, A.; Skalli, W.; Wang, X. An assessment of the realism of digital human manikins used for simulation in ergonomics. Ergonomics; 2015; 58, pp. 1897-1909. [DOI: https://dx.doi.org/10.1080/00140139.2015.1038306] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26018142]

44. Lind, C.M.; Abtahi, F.; Forsman, M. Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics—An Overview of Current Applications, Challenges, and Future Opportunities. Sensors; 2023; 23, 4259. [DOI: https://dx.doi.org/10.3390/s23094259]

45. Lu, J.K.; Wang, W.; Goh, J.; Maier, A.B. A practical guide for selecting continuous monitoring wearable devices for community-dwelling adults. Heliyon; 2024; 10, e33488. [DOI: https://dx.doi.org/10.1016/j.heliyon.2024.e33488]

46. Brunzini, A.; Peruzzini, M.; Grandi, F.; Khamaisi, R.K.; Pellicciari, M. A Preliminary Experimental Study on the Workers’ Workload Assessment to Design Industrial Products and Processes. Appl. Sci.; 2021; 11, 12066. [DOI: https://dx.doi.org/10.3390/app112412066]

47. Kowalczuk, P.; Musial, J. The impact of self-conscious emotions on the continuance intention of digital voice assistants in private and public contexts. Comput. Hum. Behav. Rep.; 2024; 15, 100450. [DOI: https://dx.doi.org/10.1016/j.chbr.2024.100450]

48. Longo, F.; Padovano, A.; Umbrello, S. Value-Oriented and Ethical Technology Engineering in Industry 5.0: A Human-Centric Perspective for the Design of the Factory of the Future. Appl. Sci.; 2020; 10, 4182. [DOI: https://dx.doi.org/10.3390/app10124182]

49. Brunetti, D.; Gena, C.; Vernero, F. Smart Interactive Technologies in the Human-Centric Factory 5.0: A Survey. Appl. Sci.; 2022; 12, 7965. [DOI: https://dx.doi.org/10.3390/app12167965]

50. Teixeira, D.; Ferreira, J.; Gonçalves, R. Monitoring of shop-floor workers postural stability through the use of smart soles. IFAC-PapersOnLine; 2022; 55, pp. 2234-2239. [DOI: https://dx.doi.org/10.1016/j.ifacol.2022.10.040]

51. Chen, H.; Schall, M.C.; Fethke, N.B. Gyroscope vector magnitude: A proposed method for measuring angular velocities. Appl. Ergon.; 2023; 109, 103981. [DOI: https://dx.doi.org/10.1016/j.apergo.2023.103981] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36739779]

52. ISO 11226 Ergonomics—Evaluation of Static Working Postures; ISO: Geneva, Switzerland, 2000; Available online: https://www.iso.org/standard/25573.html (accessed on 10 February 2025).

53. Pistolesi, F.; Baldassini, M.; Lazzerini, B. A human-centric system combining smartwatch and LiDAR data to assess the risk of musculoskeletal disorders and improve ergonomics of Industry 5.0 manufacturing workers. Comput. Ind.; 2024; 155, 104042. [DOI: https://dx.doi.org/10.1016/j.compind.2023.104042]

54. Peruzzini, M.; Grandi, F.; Pellicciari, M. Exploring the potential of Operator 4.0 interface and monitoring. Comput. Ind. Eng.; 2020; 139, 105600. [DOI: https://dx.doi.org/10.1016/j.cie.2018.12.047]

55. Baldauf, M.; Müller, S.; Seeliger, A.; Küng, T.; Michel, A.; Züllig, W. Human Interventions in the Smart Factory—A Case Study on Co-Designing Mobile and Wearable Monitoring Systems with Manufacturing Staff. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems; CHI EA ’21 Association for Computing Machinery: New York, NY, USA, 2021; pp. 1-6. [DOI: https://dx.doi.org/10.1145/3411763.3451774]

56. Fröbel, F.; Beuthel, M.; Joost, G. Wearable Mapping Suit: Body Mapping for Identification Wearables. CHItaly 2021: 14th Biannual Conference of the Italian SIGCHI Chapter, Bolzano, Italy, 11–13 July 2021; CHItaly ’21 Association for Computing Machinery: New York, NY, USA, 2021; pp. 1-7. [DOI: https://dx.doi.org/10.1145/3464385.3464729]

57. Fang, W.; Zheng, L.; Xu, J. Self-contained optical-inertial motion capturing for assembly planning in digital factory. Int. J. Adv. Manuf. Technol.; 2017; 93, pp. 1243-1256. [DOI: https://dx.doi.org/10.1007/s00170-017-0526-4]

58. Simonetto, M.; Arena, S.; Peron, M. A methodological framework to integrate motion capture system and virtual reality for assembly system 4.0 workplace design. Saf. Sci.; 2022; 146, 105561. [DOI: https://dx.doi.org/10.1016/j.ssci.2021.105561]

59. Yu, M.; Zhou, R.; Wang, H.; Zhao, W. An evaluation for VR glasses system user experience: The influence factors of interactive operation and motion sickness. Appl. Ergon.; 2019; 74, pp. 206-213. [DOI: https://dx.doi.org/10.1016/j.apergo.2018.08.012] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30487101]

60. Nagy, L.; Ruppert, T.; Abonyi, J. Human-centered knowledge graph-based design concept for collaborative manufacturing. Proceedings of the 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA); Stuttgart, Germany, 6–9 September 2022; pp. 1-8. [DOI: https://dx.doi.org/10.1109/ETFA52439.2022.9921484]

61. Leskovský, R.; Kučera, E.; Haffner, O.; Matišák, J.; Rosinová, D.; Stark, E. A Contribution to Workplace Ergonomics Evaluation Using Multimedia Tools and Virtual Reality. Proceedings of the 2019 Federated Conference on Computer Science and Information Systems (FedCSIS); Leipzig, Germany, 1–4 September 2019; pp. 317-326. [DOI: https://dx.doi.org/10.15439/2019F292]

62. Ottogalli, K.; Rosquete, D.; Rojo, J.; Amundarain, A.; Rodríguez, J.M.; Borro, D. Virtual reality simulation of human-robot coexistence for an aircraft final assembly line: Process evaluation and ergonomics assessment. Int. J. Comput. Integr. Manuf.; 2021; 34, pp. 975-995. [DOI: https://dx.doi.org/10.1080/0951192X.2021.1946855]

63. Williams, G.; Riaz, M.; Drini, E.; Riches, S. Virtual reality relaxation for mental health staff in complex care services: A feasibility and acceptability study. Ment. Health Prev.; 2024; 33, 200318. [DOI: https://dx.doi.org/10.1016/j.mhp.2023.200318]

64. Longo, F.; Nicoletti, L.; Padovano, A. Smart operators in industry 4.0: A human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context. Comput. Ind. Eng.; 2017; 113, pp. 144-159. [DOI: https://dx.doi.org/10.1016/j.cie.2017.09.016]

65. Xu, S.; Lu, Y.; Yu, C. Augmented reality-assisted cloud additive manufacturing with digital twin technology for multi-stakeholder value Co-creation in product innovation. Heliyon; 2024; 10, e25722. [DOI: https://dx.doi.org/10.1016/j.heliyon.2024.e25722]

66. Pasquale, V.D.; De Simone, V.; Franciosi, C.; Morra, P.; Miranda, S. Augmented and Virtual Reality to support Corrective and Preventive Actions in maintenance: A framework proposal. Procedia Comput. Sci.; 2024; 232, pp. 1879-1889. [DOI: https://dx.doi.org/10.1016/j.procs.2024.02.010]

67. Ras, E.; Wild, F.; Stahl, C.; Baudet, A. Bridging the Skills Gap of Workers in Industry 4.0 by Human Performance Augmentation Tools: Challenges and Roadmap. Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments; Rhodes, Greece, 21–23 June 2017; PETRA ’17 Association for Computing Machinery: New York, NY, USA, 2017; pp. 428-432. [DOI: https://dx.doi.org/10.1145/3056540.3076192]

68. Ageing and Health. Available online: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health (accessed on 10 December 2024).

69. Granata, I.; Faccio, M.; Boschetti, G. Industry 5.0: Prioritizing human comfort and productivity through collaborative robots and dynamic task allocation. Procedia Comput. Sci.; 2024; 232, pp. 2137-2146. [DOI: https://dx.doi.org/10.1016/j.procs.2024.02.144]

70. Alves, J.; Lima, T.M.; Gaspar, P.D. The sociodemographic challenge in human-centred production systems—A systematic literature review. Theor. Issues Ergon. Sci.; 2022; 25, pp. 44-66. [DOI: https://dx.doi.org/10.1080/1463922X.2022.2148178]

71. Peruzzini, M.; Pellicciari, M. A framework to design a human-centred adaptive manufacturing system for aging workers. Adv. Eng. Inform.; 2017; 33, pp. 330-349. [DOI: https://dx.doi.org/10.1016/j.aei.2017.02.003]

72. Bakator, M.; Ćoćkalo, D.; Makitan, V.; Stanisavljev, S.; Nikolić, M. The three pillars of tomorrow: How Marketing 5.0 builds on Industry 5.0 and impacts Society 5.0?. Heliyon; 2024; 10, e36543. [DOI: https://dx.doi.org/10.1016/j.heliyon.2024.e36543] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39281657]

73. Gualtieri, L.; Rauch, E.; Vidoni, R. Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review. Robot. Comput.-Integr. Manuf.; 2021; 67, 101998. [DOI: https://dx.doi.org/10.1016/j.rcim.2020.101998]

74. Proia, S.; Carli, R.; Cavone, G.; Dotoli, M. Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey. IEEE Trans. Autom. Sci. Eng.; 2022; 19, pp. 1798-1819. [DOI: https://dx.doi.org/10.1109/TASE.2021.3131011]

75. Nourmohammadi, A.; Fathi, M.; Ng, A.H.C. Balancing and scheduling assembly lines with human-robot collaboration tasks. Comput. Oper. Res.; 2022; 140, 105674. [DOI: https://dx.doi.org/10.1016/j.cor.2021.105674]

76. Djogdom, G.V.T.; Meziane, R.; Otis, M.J.-D. Robust dynamic robot scheduling for collaborating with humans in manufacturing operations. Robot. Comput. Integr. Manuf.; 2024; 88, 102734. [DOI: https://dx.doi.org/10.1016/j.rcim.2024.102734]

77. Navarro, J.; Heuveline, L.; Avril, E.; Cegarra, J. Influence of human-machine interactions and task demand on automation selection and use. Ergonomics; 2018; 61, pp. 1601-1612. [DOI: https://dx.doi.org/10.1080/00140139.2018.1501517]

78. Faccio, M.; Granata, I.; Menini, A.; Milanese, M.; Rossato, C.; Bottin, M.; Minto, R.; Pluchino, P.; Gamberini, L.; Boschetti, G. et al. Human factors in cobot era: A review of modern production systems features. J. Intell. Manuf.; 2023; 34, pp. 85-106. [DOI: https://dx.doi.org/10.1007/s10845-022-01953-w]

79. Gualtieri, L.; Rauch, E.; Vidoni, R.; Matt, D.T. An evaluation methodology for the conversion of manual assembly systems into human-robot collaborative workcells. Procedia Manuf.; 2019; 38, pp. 358-366. [DOI: https://dx.doi.org/10.1016/j.promfg.2020.01.046]

80. Cheng, P.D.C.; Sibona, F.; Indri, M. A framework for safe and intuitive human-robot interaction for assistant robotics. Proceedings of the 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA); Stuttgart, Germany, 6–9 September 2022; pp. 1-4. [DOI: https://dx.doi.org/10.1109/ETFA52439.2022.9921569]

81. Fournier, É.; Jeoffrion, C.; Hmedan, B.; Pellier, D.; Fiorino, H.; Landry, A. Human-cobot collaboration’s impact on success, time completion, errors, workload, gestures and acceptability during an assembly task. Appl. Ergon.; 2024; 119, 104306. [DOI: https://dx.doi.org/10.1016/j.apergo.2024.104306] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38714102]

82. Baltrusch, S.; Krause, F.; de Vries, A.; Dijk, W.; Looze, M.P. What about the Human in Human Robot Collaboration? A literature review on HRC’s effects on aspects of job quality. Ergonomics; 2021; 65, pp. 719-740. [DOI: https://dx.doi.org/10.1080/00140139.2021.1984585] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34546152]

83. Eich, A.; Klichowicz, A.; Bocklisch, F. How automation level influences moral decisions of humans collaborating with industrial robots in different scenarios. Front. Psychol.; 2023; 14, 1107306.Available online: https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1107306 (accessed on 9 November 2023). [DOI: https://dx.doi.org/10.3389/fpsyg.2023.1107306] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36968758]

84. Bounouar, M.; Bearee, R.; Siadat, A.; Benchekroun, T.-H. On the role of human operators in the design process of cobotic systems. Cogn. Technol. Work; 2022; 24, pp. 57-73. [DOI: https://dx.doi.org/10.1007/s10111-021-00691-y]

85. Colim, A.; Morgado, R.; Carneiro, P.; Costa, N.; Faria, C.; Sousa, N.; Rocha, L.A.; Arezes, P. Lean Manufacturing and Ergonomics Integration: Defining Productivity and Wellbeing Indicators in a Human–Robot Workstation. Sustainability; 2021; 13, 1931. [DOI: https://dx.doi.org/10.3390/su13041931]

86. Weckenborg, C.; Spengler, T.S. Assembly Line Balancing with Collaborative Robots under consideration of Ergonomics: A cost-oriented approach. IFAC-PapersOnLine; 2019; 52, pp. 1860-1865. [DOI: https://dx.doi.org/10.1016/j.ifacol.2019.11.473]

87. Caruana, L.; Francalanza, E. A Safety 4.0 Approach for Collaborative Robotics in the Factories of the Future. Procedia Comput. Sci.; 2023; 217, pp. 1784-1793. [DOI: https://dx.doi.org/10.1016/j.procs.2022.12.378]

88. Park, Y.W.; Shintaku, J. Sustainable Human–Machine Collaborations in Digital Transformation Technologies Adoption: A Comparative Case Study of Japan and Germany. Sustainability; 2022; 14, 17. [DOI: https://dx.doi.org/10.3390/su141710583]

89. Silva, A.; Simões, A.C.; Blanc, R. Supporting decision-making of collaborative robot (cobot) adoption: The development of a framework. Technol. Forecast. Soc. Change; 2024; 204, 123406. [DOI: https://dx.doi.org/10.1016/j.techfore.2024.123406]

90. Berx, N.; Decré, W.; Pintelon, L. A tool to evaluate industrial cobot safety readiness from a system-wide perspective: An empirical validation. Saf. Sci.; 2024; 170, 106380. [DOI: https://dx.doi.org/10.1016/j.ssci.2023.106380]

91. Webster, K.L.W.; Haut, E.R. Chapter 7—Human factors and ergonomics in the operating room. Handbook of Perioperative and Procedural Patient Safety; Sanchez, J.A.; Higgins, R.S.D.; Kent, P.S. Elsevier: Amsterdam, The Netherlands, 2024; pp. 75-86. [DOI: https://dx.doi.org/10.1016/B978-0-323-66179-9.00011-7]

92. Gašová, M.; Gašo, M.; Štefánik, A. Advanced Industrial Tools of Ergonomics Based on Industry 4.0 Concept. Procedia Eng.; 2017; 192, pp. 219-224. [DOI: https://dx.doi.org/10.1016/j.proeng.2017.06.038]

93. Reiman, A.; Kaivo-oja, J.; Parviainen, E.; Takala, E.-P.; Lauraeus, T. Human factors and ergonomics in manufacturing in the industry 4.0 context—A scoping review. Technol. Soc.; 2021; 65, 101572. [DOI: https://dx.doi.org/10.1016/j.techsoc.2021.101572]

94. Xu, S.; Hall, N.G. Fatigue, personnel scheduling and operations: Review and research opportunities. Eur. J. Oper. Res.; 2021; 295, pp. 807-822. [DOI: https://dx.doi.org/10.1016/j.ejor.2021.03.036]

95. Neumann, W.P.; Winkelhaus, S.; Grosse, E.H.; Glock, C.H. Industry 4.0 and the human factor—A systems framework and analysis methodology for successful development. Int. J. Prod. Econ.; 2021; 233, 107992. [DOI: https://dx.doi.org/10.1016/j.ijpe.2020.107992]

96. Broday, E.E. Participatory Ergonomics in the context of Industry 4.0: A literature review. Theor. Iss. Ergon. Sci.; 2021; 22, pp. 237-250. [DOI: https://dx.doi.org/10.1080/1463922X.2020.1801886]

97. Kong, F. Development of metric method and framework model of integrated complexity evaluations of production process for ergonomics workstations. Int. J. Prod. Res.; 2019; 57, pp. 2429-2445. [DOI: https://dx.doi.org/10.1080/00207543.2018.1519266]

98. Baratta, A.; Cimino, A.; Longo, F.; Mirabelli, G.; Nicoletti, L. Task Allocation in Human-Robot Collaboration: A Simulation-based approach to optimize Operator’s Productivity and Ergonomics. Procedia Comput. Sci.; 2024; 232, pp. 688-697. [DOI: https://dx.doi.org/10.1016/j.procs.2024.01.068]

99. Cimini, C.; Lagorio, A.; Cavalieri, S.; Riedel, O.; Pereira, C.E.; Wang, J. Human-technology integration in smart manufacturing and logistics: Current trends and future research directions. Comput. Ind. Eng.; 2022; 169, 108261. [DOI: https://dx.doi.org/10.1016/j.cie.2022.108261]

100. Kretschmer, V.; Plewan, T.; Rinkenauer, G.; Maettig, B. Smart Palletisation: Cognitive Ergonomics in Augmented Reality Based Palletising. Intelligent Human Systems Integration; Advances in Intelligent Systems and Computing Karwowski, W.; Ahram, T. Springer International Publishing: Cham, Switzerland, 2018; pp. 355-360. [DOI: https://dx.doi.org/10.1007/978-3-319-73888-8_55]

101. Lagomarsino, M.; Lorenzini, M.; De Momi, E.; Ajoudani, A. An Online Framework for Cognitive Load Assessment in Industrial Tasks. Robot. Comput.-Integr. Manuf.; 2022; 78, 102380. [DOI: https://dx.doi.org/10.1016/j.rcim.2022.102380]

102. Reyes, J.; Mula, J.; Díaz-Madroñero, M. Optimisation Modeling for Lean, Resilient, Flexible and Sustainable Supply Chain Planning. IoT and Data Science in Engineering Management; Lecture Notes on Data Engineering and Communications, Technologies; Márquez, F.P.G.; Ramírez, I.S.; Sánchez, P.J.B.; del Río, A.M. Springer International Publishing: Cham, Switzerland, 2023; pp. 341-345. [DOI: https://dx.doi.org/10.1007/978-3-031-27915-7_60]

103. Nagaraj, T.S.; Jeyapaul, R. An empirical investigation on association between human factors, ergonomics and lean manufacturing. Prod. Plan. Control; 2021; 32, pp. 1337-1351. [DOI: https://dx.doi.org/10.1080/09537287.2020.1810815]

104. Vicente, I.; Godina, R.; Gabriel, A.T. Applications and future perspectives of integrating Lean Six Sigma and Ergonomics. Saf. Sci.; 2024; 172, 106418. [DOI: https://dx.doi.org/10.1016/j.ssci.2024.106418]

105. Grandi, F.; Peruzzini, M.; Campanella, C.; Pellicciari, M. Application of Innovative Tools to Design Ergonomic Control Dashboards. Transdisciplinary Engineering for Complex Socio-Technical Systems–Real-Life Applications; IOS Press: Amsterdam, The Netherlands, 2020; pp. 193-200. [DOI: https://dx.doi.org/10.3233/ATDE200077]

106. Firescu, V.; Gaşpar, M.-L.; Crucianu, I.; Rotariu, E. Collaboration Between Humans and Robots in Organizations: A Macroergonomic, Emotional, and Spiritual Approach. Front. Psychol.; 2022; 13, 855768.Available online: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.855768 (accessed on 10 September 2023). [DOI: https://dx.doi.org/10.3389/fpsyg.2022.855768]

107. Pouyakian, M. Cybergonomics: Proposing and justification of a new name for the ergonomics of Industry 4.0 technologies. Front. Public Health; 2022; 10, 1012985.Available online: https://www.frontiersin.org/articles/10.3389/fpubh.2022.1012985 (accessed on 10 September 2023). [DOI: https://dx.doi.org/10.3389/fpubh.2022.1012985] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36408027]

108. Galey, L.; Terquem, V.; Barcellini, F. A Social Design Approach: Enhancement of Local Social Dialogue on the Transformation of Work by Digital Technology. Relat. Ind./Ind. Relat.; 2022; 77, pp. 1-20. [DOI: https://dx.doi.org/10.7202/1094211ar]

109. Feldmann, F.; Seitz, R.; Kretschmer, V.; Bednorz, N.; Hompel, M.T. Ergonomic Evaluation of Body Postures in Order Picking Systems Using Motion Capturing. Proceedings of the 2019 Prognostics and System Health Management Conference (PHM-Paris); Paris, France, 2–5 May 2019; pp. 204-209. [DOI: https://dx.doi.org/10.1109/PHM-Paris.2019.00041]

110. Molino, M.; Cortese, C.G.; Ghislieri, C. Technology Acceptance and Leadership 4.0: A Quali-Quantitative Study. Int. J. Environ. Res. Public Health; 2021; 18, 10845. [DOI: https://dx.doi.org/10.3390/ijerph182010845] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34682588]

111. De Lombaert, T.; Braekers, K.; De Koster, R.; Ramaekers, K. In pursuit of humanised order picking planning: Methodological review, literature classification and input from practice. Int. J. Prod. Res.; 2023; 61, pp. 3300-3330. [DOI: https://dx.doi.org/10.1080/00207543.2022.2079437]

112. Johnsen, S.O.; Kilskar, S.S.; Fossum, K.R. Missing focus on Human Factors—Organizational and cognitive ergonomics—In the safety management for the petroleum industry. Proc. Inst. Mech. Eng. Part O J. Risk Reliab.; 2017; 231, pp. 400-410. [DOI: https://dx.doi.org/10.1177/1748006X17698066] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29278242]

113. Zafar, M.H.; Langås, E.F.; Sanfilippo, F. Exploring the synergies between collaborative robotics, digital twins, augmentation, and industry 5.0 for smart manufacturing: A state-of-the-art review. Robot. Comput.-Integr. Manuf.; 2024; 89, 102769. [DOI: https://dx.doi.org/10.1016/j.rcim.2024.102769]

114. Adriaensen, A.; Decré, W.; Pintelon, L. Can Complexity-Thinking Methods Contribute to Improving Occupational Safety in Industry 4.0? A Review of Safety Analysis Methods and Their Concepts. Safety; 2019; 5, 65. [DOI: https://dx.doi.org/10.3390/safety5040065]

115. Nwakanma, C.I.; Islam, F.B.; Maharani, M.P.; Lee, J.-M.; Kim, D.-S. Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor. Appl. Sci.; 2021; 11, 3662. [DOI: https://dx.doi.org/10.3390/app11083662]

116. Cao, H.-L.; Elprama, S.A.; Scholz, C.; Siahaya, P.; El Makrini, I.; Jacobs, A.; Ajoudani, A.; Vanderborght, B. Designing interaction interface for supportive human-robot collaboration: A co-creation study involving factory employees. Comput. Ind. Eng.; 2024; 192, 110208. [DOI: https://dx.doi.org/10.1016/j.cie.2024.110208]

117. Padovano, A.; Cardamone, M.; Woschank, M.; Pacher, C. Exploring Human-Centricity in Industry 5.0: Empirical Insights from a Social Media Discourse. Procedia Comput. Sci.; 2024; 232, pp. 1859-1868. [DOI: https://dx.doi.org/10.1016/j.procs.2024.02.008]

118. Guo, J.; Leng, J.; Zhao, J.L.; Zhou, X.; Yuan, Y.; Lu, Y.; Mourtzis, D.; Qi, Q.; Huang, S.; Song, X. et al. Industrial metaverse towards Industry 5.0: Connotation, architecture, enablers, and challenges. J. Manuf. Syst.; 2024; 76, pp. 25-42. [DOI: https://dx.doi.org/10.1016/j.jmsy.2024.07.007]

119. Alves, J.; Lima, T.; Gaspar, P. Is Industry 5.0 a Human-Centred Approach? A Systematic Review. Processes; 2023; 11, 193. [DOI: https://dx.doi.org/10.3390/pr11010193]

120. Conforti, I.; Mileti, I.; Del Prete, Z.; Palermo, E. Assessing ergonomics and biomechanical risk in manual handling of loads through a wearable system. Proceedings of the 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT); Naples, Italy, 4–6 June 2019; pp. 388-393. [DOI: https://dx.doi.org/10.1109/METROI4.2019.8792843]

121. Scafà, M.; Papetti, A.; Brunzini, A.; Germani, M. How to improve worker’s well-being and company performance: A method to identify effective corrective actions. Procedia CIRP; 2019; 81, pp. 162-167. [DOI: https://dx.doi.org/10.1016/j.procir.2019.03.029]

122. Fabio, G.; Margherita, P.; Luca, Z.; Marcello, P. An automatic procedure based on virtual ergonomic analysis to promote human-centric manufacturing. Procedia Manuf.; 2019; 38, pp. 488-496. [DOI: https://dx.doi.org/10.1016/j.promfg.2020.01.062]

123. Yitmen, I.; Almusaed, A.; Alizadehsalehi, S. Investigating the Causal Relationships among Enablers of the Construction 5.0 Paradigm: Integration of Operator 5.0 and Society 5.0 with Human-Centricity, Sustainability, and Resilience. Sustainability; 2023; 15, 9105. [DOI: https://dx.doi.org/10.3390/su15119105]

124. Zambiasi, L.P.; Rabelo, R.J.; Zambiasi, S.P.; Lizot, R. Supporting Resilient Operator 5.0: An Augmented Softbot Approach. Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action; IFIP Advances in Information and Communication Technology Kim, D.Y.; von Cieminski, G.; Romero, D. Springer Nature Switzerland: Cham, Switzerland, 2022; pp. 494-502. [DOI: https://dx.doi.org/10.1007/978-3-031-16411-8_57]

125. Wrobel-Lachowska, M.; Wisniewski, Z.; Polak-Sopinska, A. The Role of the Lifelong Learning in Logistics 4.0. Advances in Human Factors in Training, Education, and Learning Sciences; Advances in Intelligent Systems and, Computing Andre, T. Springer International Publishing: Cham, Switzerland, 2018; pp. 402-409. [DOI: https://dx.doi.org/10.1007/978-3-319-60018-5_39]

126. Allemang--Trivalle, A.; Donjat, J.; Bechu, G.; Coppin, G.; Chollet, M.; Klaproth, O.W.; Mitschke, A.; Schirrmann, A.; Cao, C.G. Modeling Fatigue in Manual and Robot-Assisted Work for Operator 5.0. IISE Trans. Occup. Ergon. Hum. Factors; 2024; 12, pp. 135-147. [DOI: https://dx.doi.org/10.1080/24725838.2024.2321460] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38441578]

127. Gazzaneo, L.; Padovano, A.; Umbrello, S. Designing Smart Operator 4.0 for Human Values: A Value Sensitive Design Approach. Procedia Manuf.; 2020; 42, pp. 219-226. [DOI: https://dx.doi.org/10.1016/j.promfg.2020.02.073]

128. Panagou, S.; Fruggiero, F.; Lambiase, A. The Sustainable Role of Human Factor in I4.0 scenarios. Procedia Comput. Sci.; 2021; 180, pp. 1013-1023. [DOI: https://dx.doi.org/10.1016/j.procs.2021.01.354]

129. Calzavara, M.; Battini, D.; Bogataj, D.; Sgarbossa, F.; Zennaro, I. Ageing workforce management in manufacturing systems: State of the art and future research agenda. Int. J. Prod. Res.; 2020; 58, pp. 729-747. [DOI: https://dx.doi.org/10.1080/00207543.2019.1600759]

130. Ciccarelli, M.; Papetti, A.; Scoccia, C.; Menchi, G.; Mostarda, L.; Palmieri, G.; Germani, M. A system to improve the physical ergonomics in Human-Robot Collaboration. Procedia Comput. Sci.; 2022; 200, pp. 689-698. [DOI: https://dx.doi.org/10.1016/j.procs.2022.01.267]