1. Introduction
The concept of Industry 5.0 (I5.0) represents a profound evolution in the manufacturing sector, moving beyond the technological efficiencies of Industry 4.0 (I4.0) to emphasize a more balanced integration of human ingenuity and advanced technology. While I4.0 primarily focuses on automation, interconnectivity, and data-driven decision-making through enabling technologies such as the Industrial Internet of Things (IIoT), Big Data Analytics (BDA), and cyber-physical systems (CPS) [1], I5.0 extends this foundation by emphasizing human-centric design. This shift is not only about enhancing automation and interconnectivity but also critically emphasizes the reintroduction of human skills and perspectives into the workflow [2]. I5.0 seeks to merge the strengths of people and machines to create more flexible, efficient, and sustainable systems that are fundamentally oriented toward improving not just productivity, but also the quality of the workplace itself [3]. Unlike I4.0, which targets operational efficiency through autonomous systems, I5.0 seeks to create a collaborative ecosystem where humans and machines work in harmony. Machines support human tasks by automating repetitive operations, while humans provide insight, decision-making, and creativity, enhancing both productivity and job satisfaction [4]. The core distinction lies in how humans are positioned within these industrial paradigms. In I4.0, humans are often supervisors or operators of automated systems, while in I5.0, humans are integral to the production system itself [5]. Advanced technologies, such as Human Digital Twins (HDTs), bridge this human–machine divide by digitally representing workers’ skills, cognitive functions, and well-being [4]. I5.0 emphasizes three core pillars: human-centric, sustainability, and resilience as defined by the European Commission [6]. I5.0 extends the sustainability focus beyond environmental dimensions by emphasizing social sustainability, which encompasses the well-being of the workforce, promoting an inclusive, equitable, and fulfilling work environment [7].
The focus on social sustainability in this study is due to the fact that human-centric manufacturing inherently requires not only technological innovation but also the social well-being of the workforce. Addressing social dimensions ensures that technological advancements in Industry 5.0 create value that benefits both businesses and communities. However, while the conceptual foundations of I5.0 have been explored, the integration of its technological enablers with human-centric approaches remains underdeveloped. A critical research gap exists in establishing comprehensive frameworks that align HDTs with I4.0’s enabling technologies, ensuring their applicability across diverse industrial sectors.
Within the I5.0 paradigm, HDTs emerge as a significant innovation. HDTs are advanced digital replicas that mirror the biological, psychological, and emotional characteristics of their human counterparts, enabling a deeper and more effective integration of human factors into the manufacturing process [8]. HDTs facilitate nuanced interactions between humans and machines, allowing for enhanced decision-making, improved ergonomic designs, and personalized work experiences [9]. These capabilities make HDTs a critical tool in realizing the potential of I5.0, where technology not only supports but also enhances human capabilities. By ensuring that technological advancements align with human needs, HDTs help create a more adaptive, intuitive, and responsive manufacturing environment [10]. Despite the growing interest in HDTs, there is still limited understanding of how these digital representations of human factors can be operationalized in real-world manufacturing environments. Addressing this gap requires a deeper investigation into the enabling technologies, system architectures, and industry-specific use cases that drive HDT adoption.
The aim of this review is to provide a comprehensive examination of the current state and future prospects of HDTs within the context of I5.0. Through a meticulous synthesis of existing research, this paper will explore the evolution, implementation, and impact of HDTs. It will detail the frameworks, technological foundations, and practical applications of HDTs while also addressing the challenges they face. This analysis will highlight how HDTs contribute to the broader goals of social sustainability in manufacturing, underscoring their role in enhancing the human aspects of industrial systems. By mapping out the interaction between HDTs and I4.0’s technologies, this review seeks to illuminate the pathways through which modern manufacturing can evolve into a more humane, responsive, and sustainable practice. By addressing these challenges, our study contributes both theoretically and practically, offering a structured framework for understanding HDT development while providing actionable insights to guide future industrial implementations in alignment with Industry 5.0 principles.
2. Research Context
2.1. Industry 5.0 and Human-Centric Manufacturing
I5.0 represents a transformative evolution in the industrial landscape, shifting the focus from purely technology-driven innovation to a more balanced, human-centered approach. Unlike its predecessor, I4.0, which emphasized automation, big data, and cyber-physical systems, I5.0 seeks to harmonize technological advancements with the intrinsic needs and values of humans. It introduces the concept of human–machine collaboration, where technologies such as Artificial Intelligence (AI), Digital Twins (DTs), and collaborative robots (cobots) are designed not to replace humans but to augment their capabilities and improve their quality of life in industrial settings [2]. This paradigm aligns with broader societal goals, emphasizing inclusivity, sustainability, and resilience while fostering workplaces that prioritize creativity, well-being, and ethical engagement [11]. At the heart of I5.0 lies the principle of human-centricity, which reflects a commitment to placing workers’ needs, abilities, and well-being at the forefront of technological innovation [12]. Human-centric manufacturing recognizes that humans are not just operators of machines but are integral contributors to the creative and decision-making processes that drive industrial success [13]. This approach fosters environments that enhance worker satisfaction, empower individuals through upskilling and continuous learning, and provide meaningful roles in increasingly automated systems [14]. Sustainability, as a cornerstone of I5.0, extends beyond environmental concerns to encompass economic and social dimensions [15]. While environmental sustainability has traditionally dominated industrial discussions, I5.0 explicitly broadens this concept by incorporating social sustainability as a key pillar. Social sustainability focuses on creating equitable, inclusive, and supportive work environments by addressing disparities in access to opportunities, fostering worker empowerment, and ensuring safe and healthy conditions [16]. In human-centric manufacturing, this involves designing systems that prioritize the well-being of workers while balancing productivity. By integrating human values into industrial design, I5.0 advances goals such as reducing workplace inequities and promoting ethical labor practices [17]. While social sustainability encompasses broader societal goals (beyond the workplace), its implementation in Industry 5.0 aligns closely with human-centric design. This integrated approach ensures that industrial progress is both socially responsible and technologically advanced, creating sustainable business models that prioritize people and their long-term well-being [18]. Moreover, I5.0 emphasizes resilience as a complementary concept to sustainability and the human-centric approach. Resilience refers to the ability of the environments to adapt, recover, and evolve in response to challenges such as technological disruptions, market fluctuations, or global crises [19]. Resilience supports human-centricity and social sustainability, allowing organizations to manage disruptions effectively while safeguarding employee well-being and professional development through continuous learning, safe working conditions, and responsive technologies that support both personal and organizational growth [20]. For example, DTs and HDTs enable real-time feedback mechanisms that monitor the physical and emotional states of workers, promoting interventions that improve safety and well-being [21]. This not only aligns with sustainability goals but also creates a more engaged and motivated workforce. Additionally, technologies such as Lifecycle Analysis (LCA) and predictive modeling allow industries to optimize resource usage and minimize waste, further strengthening the environmental and social sustainability of manufacturing systems [22]. LCA, particularly through the Social Life Cycle Assessment (S-LCA) framework, evaluates the social impacts of products across their entire life cycle, considering stakeholders such as workers, consumers, and local communities. This approach supports the identification of social risks, the promotion of fair labor practices, and the enhancement of community well-being, contributing directly to social sustainability [23]. These core values aim to enhance manufacturing by integrating human flexibility with machine precision and advanced information technologies, creating a framework for smart, sustainable, and resilient manufacturing systems [24]. These principles collectively address several challenges as industries strive to adapt to this new paradigm. Key challenges related to I5.0 include the need for workforce skill development, the adoption of advanced technologies, significant investment requirements, and rigorous security measures to protect increasingly interconnected ICT systems [25].
2.2. The Role of Human Digital Twins in the Human-Centric Transition
The DT technology, which began to emerge during the I4.0 era, has seen significant advancements in the context of I5.0. Originally conceptualized by NASA in 2010, DTs are virtual models that replicate the physical characteristics and dynamics of real-world entities [26]. Within the I4.0 context, nine foundational technological pillars have been identified [27]: BDA, Autonomous Robots, simulation, horizontal and vertical (H/V) system integration, the IIoT, cybersecurity, the Cloud, Additive Manufacturing, and Augmented Reality (AR). These pillars provide the enabling technologies that underpin the evolution of DTs and their applications in industrial systems. In manufacturing, DTs have revolutionized supply chain management, design processes, and risk management by enabling precise planning, accurate forecasting, and the simulation of potential hazards and inefficiencies [28]. Building on the foundation of DT technology, the HDT has emerged as a transformative concept tailored to align with the human-centric focus of I5.0. Unlike traditional DTs, which focus on machines, infrastructure, products, processes, and shop floors [29], HDTs integrate human behaviors, characteristics, and real-time interactions into virtual models. By incorporating physiological, cognitive, and emotional data, HDTs provide a sophisticated tool for enhancing human–machine interaction and creating adaptive industrial environments [13]. HDTs enable real-time monitoring of workers’ physical and mental states through wearable IIoT devices and advanced sensors, capturing metrics such as heart rate, posture, fatigue, and stress levels. This data is analyzed using AI algorithms to assess worker well-being and predict potential risks, allowing for proactive interventions to enhance workplace safety and efficiency. For instance, HDTs can identify early signs of fatigue and recommend task adjustments, breaks, or ergonomic changes to reduce injury risks. In doing so, HDTs significantly contribute to safer and more productive industrial environments [30]. Beyond safety, HDTs also address cognitive and emotional factors by simulating human decision-making processes and responses to environmental stimuli. These capabilities are particularly valuable in scenarios that demand high levels of mental focus or involve complex problem-solving tasks. By modeling stress levels, motivation, and cognitive load, HDTs enable systems to adapt dynamically to individual worker needs, improving overall job satisfaction and reducing error rates. Additionally, HDTs enhance collaboration with cobots and other intelligent systems, ensuring that human–machine interactions are seamless, intuitive, and productive [31]. HDTs are also integral to training and skill development. By leveraging immersive simulations, HDTs provide workers with virtual environments where they can practice complex tasks, troubleshoot scenarios, and refine skills in a risk-free setting. This approach not only accelerates learning but also ensures that workers are better prepared for real-world challenges. Furthermore, HDTs optimize task scheduling and allocation by aligning assignments with individual capabilities, preferences, and performance data, leading to improved productivity and reduced worker fatigue [32]. Despite their potential, the implementation of HDTs is not without challenges. Ethical concerns regarding data privacy, consent, and potential misuse of sensitive human data must be addressed through robust regulatory frameworks and transparent development practices. Additionally, the technical complexity of HDTs, including the integration of real-time data from multiple sources and the use of AI for predictive analytics, requires advanced infrastructure and interdisciplinary collaboration. Interoperability issues further complicate the widespread adoption of HDTs, as standardized frameworks for their development and deployment are still lacking [33]. Looking forward, the symbiotic relationship between DTs and HDTs will play a central role in shaping the human-centric industrial systems of the future. As these technologies continue to evolve, their integration with extended reality (XR) and other advanced interfaces promises to further revolutionize human–machine interactions [34]. By bridging the gap between physical and virtual worlds, DTs and HDTs ensure that technological advancements directly contribute to creating more adaptive, efficient, and human-friendly manufacturing environments, firmly aligning with the principles of I5.0 [35]. This holistic approach underscores the transformative potential of these technologies in fostering innovation while maintaining a strong focus on human-centric values. By facilitating real-time monitoring of workers’ physical and emotional states, HDTs directly contribute to social sustainability by ensuring safe, healthy, and inclusive work environments where human needs are prioritized.
3. Research Methodology
To optimally present the contribution of this paper in expanding and systematizing the HDT domain, the research methodology consists of a systematic literature review composed of three main phases (search criteria, article search, and article content analysis) according to the guidelines provided by [36,37]. In the first one, the investigation dimensions were chosen according to the aim of the research. A document search was performed in this research context through the query on the scientific database Scopus®, without considering any field content limitation. To search for contributions, the query was used on titles, abstracts, and keywords: TITLE-ABS-KEY ((“digital twin” OR “DT” OR “HDT” OR “human digital twin”) AND (“Industry 5.0” OR “Industry 4.0”) AND (“human centric” OR “human centred” OR “human centered” OR “human factor” OR “people centric”)). The Scopus search was conducted at the beginning of the second quarter of 2024, specifically in May. The first part of the query searches for articles that mention any of these terms. “DT” stands for Digital Twins in general, while “HDT” and “human digital twin” specify articles that discuss Digital Twins with a focus on human aspects, known as Human Digital Twins. The second part is to filter the literature concerning the fourth and fifth industrial revolutions. I4.0 focuses on automation and data exchange in manufacturing technologies (including cyber-physical systems, the Internet of Things (IoT), and cloud computing), while I5.0 builds on these by reintegrating the human touch into the technologically advanced framework, emphasizing collaboration between humans and machines. The last part searches for terms that are crucial for highlighting articles that focus on the human element—whether referred to as human-centric, human-centered, or people-centric approaches. These phrases ensure that the results will emphasize the role of human factors in the context of advanced manufacturing environments. Scientific articles published were gathered from the Scopus® scientific database (as it is the most widely used for industrial engineering and has a broader coverage [38]) to investigate how DT technologies are being integrated with human-centric approaches in the evolving landscapes of I4.0 and I5.0, thus providing a basis for further research or practical application in these areas. Figure 1 shows the research strategy used in the systematic literature review [37]. As a result, the search strategy led to a total number of 91 articles. Then, on the identified sample of papers, the first selection was conducted by reviewing titles, abstracts, and keywords, and the second screening was performed by analyzing the entire manuscript.
Using this structured query in academic databases helps systematically gather literature that covers specific intersections of technology and human factors within modern industrial frameworks. The selection was conducted based on the relevance of documents, taking into account only those contributions proposing a comprehensive overview of how digital technologies such as DTs are evolving to better accommodate and enhance human roles in industrial settings. By applying these refining criteria, the set of documents found was reduced to 47 selected articles identifying the major trends in the field. Finally, the contributions selected have been progressively sorted, labeled, integrated, and then prioritized (using the SLIP method proposed by [39] and adopted also in [40]) according to their content into five main categories, presented in Section 4.2. Then, following the approach of [40], they were analyzed through three dimensions: type of contribution proposed (framework, approach, guidelines, model, methodology, tool), I4.0 technologies adopted (according to the nine pillars presented in Section 2.2 [27]), and industry involved in the study. Among these three dimensions, the type of contribution proposed and I4.0 technologies have been selected and considered since they have already demonstrated their effectiveness in this kind of analysis, due to their recurrent use in previous systematic literature reviews [40]. The other remaining dimension (industry type) has been included in the analysis due to its relevance for the definition and characterization of the threefold research context investigated in this study.
4. Main Findings
4.1. Descriptive Analysis
Figure 2 illustrates the chronological distribution of publications, highlighting a growing trend from 2019 and peaking in 2023 with 18 publications.
This trend underscores the increasing academic interest in I5.0, which began to take shape in 2021 with its formal definition by the European Commission. The negligible publication activity before 2021 reflects the absence of an explicit framework for I5.0 during that time. The sharp rise in publications between 2021 and 2023 demonstrates the rapid adoption and exploration of I5.0 concepts, particularly enabling technologies such as DTs and their evolution into HDTs. The peak in 2023 signals a growing maturity in the field, as researchers focus on investigating frameworks, architectures, and applications of HDTs to enhance human-system integration in manufacturing. HDTs, as digital representations of human characteristics, are pivotal in advancing smart manufacturing by addressing diverse worker needs and improving system adaptability. The apparent decline in 2024 can likely be attributed to data collection limited to the first quarter.
Additionally, an analysis of geographical trends in the literature has been conducted to provide further insights. Figure 3 compares the geographical distribution of publications across selected countries, distinguishing between EU countries (France, Italy, Spain, and Finland) and a non-EU country (China). The chart shows that China leads with five publications, surpassing individual contributions from European nations. Among EU countries, France, Italy, and Spain have similar levels of contribution, each with four publications, while Finland follows closely with three. This distribution highlights China’s prominent role in advancing I5.0 research, demonstrating its strong engagement with topics such as human-centric manufacturing and enabling technologies such as HDTs. Meanwhile, European countries collectively make a significant contribution, reflecting the European Commission’s leadership in defining I5.0 and promoting its adoption across the EU. The similar publication levels within EU countries suggest a balanced effort in I5.0 research and development across the region. The distinction between EU and non-EU contributions also underscores the global nature of I5.0 research. While the EU remains a pivotal actor, particularly due to policy initiatives emphasizing sustainability and human-centricity, countries such as China are emerging as key players, leveraging their technological infrastructure and research capabilities to advance the field further.
Figure 4 shows the distribution of the selected documents according to the top six relevant journals.
Figure 5 illustrates the diverse range of industries contributing to the development of I5.0 literature. As expected, the manufacturing sector leads, leveraging the applications and opportunities provided by DTs within the I4.0/5.0 context to enhance production processes. However, contributions from other sectors, such as healthcare, are also evident, showcasing the versatility of DTs in improving operational efficiency, patient monitoring, and personalized care.
4.2. HDTs Thematic Classification Analysis
This section presents the results of the detailed analysis of macro topics concerning HDTs identified in the literature (see Table 1). To structure this investigation, articles were progressively grouped into five main categories that define the HDTs domain: HDTs and Industry 5.0, Human–machine collaboration, Human behavior, Ergonomics and Safety, and Human digital representation. Before grouping, each study was analyzed based on its type—distinguishing between Framework, Guideline, Model, Methodology, or Review—and the Main Objective of the research. This preliminary step ensured a comprehensive understanding of the diverse contributions in the field and helped identify the thematic focus of each work. The categorization by type was conducted using clear criteria; each article was classified according to its conceptual abstraction and scope. Frameworks and guidelines define broader conceptual structures or stepwise recommendations, while models and methodologies focus on specific representations or systematic procedures. Reviews consolidate existing knowledge in a domain. The Main Objective summarizes the core focus of each study, revealing whether it addresses technological advancements, human factors, or integration processes in HDTs.
The domains have been defined as part of this study, grouping the articles into thematic categories based on their focus and contribution to the HDT field. As shown in Figure 6, these domains reflect the diverse research directions and applications identified in the literature. Each article has been classified into one of these domains, providing a structured overview of the existing knowledge and highlighting the specific areas of innovation and exploration within the HDT landscape:
HDTs and Industry 5.0 focuses on the introduction of the new I5.0 paradigm and DT technologies, emphasizing their integration with human-centric principles.
Human-machine collaboration illustrates the features that ensure the correct collaboration between man and machine in industrial works.
Human behavior relates to the inclusion of human behavior and skills that workers perform within the work process.
Ergonomics and Safety is a fundamental aspect of HDT, as it most significantly characterizes of humans within the company.
Human digital representation focuses on the digital representation of humans, particularly in two aspects: Psychological health and Physiological health of workers.
The distribution of articles among the categories has been performed manually and is illustrated in Figure 6, which follows a dimensional scheme where references serve as concrete examples of relevant literature supporting each identified dimension [37].
4.2.1. HDTs and Industry 5.0
The HDTs and Industry 5.0 category includes eight studies that collectively explore how HDTs facilitate the evolution from I4.0 to I5.0 by integrating human-centric principles with advanced digital technologies. HDTs in Industry 5.0 foster social sustainability by creating environments where human well-being is continuously monitored and improved through real-time data-driven interventions, ensuring safer, fairer, and more inclusive workplaces. These papers highlight how HDTs enhance adaptability, safety, and efficiency in manufacturing systems, with each offering unique perspectives on methodologies, architectures, and applications. Some studies lay the conceptual groundwork, such as [25], which provides an overview of I5.0, emphasizing human creativity and collaboration alongside technologies such as Big Data Analytics (BDA) and simulation for decision-making and manufacturing personalization. Similarly, ref. [58] offers a bibliometric analysis tracing the evolution of DTs in supply chains, showcasing how IIoT, AI, and BDA enable human-centric applications. Other studies focus on specific architectures and methodologies, such as [49], which introduces an operator-centric DT tailored for composite manufacturing, employing simulation and H/V system integration to enhance operator decision-making and ensure seamless coordination across manufacturing stages, and [50], which proposes a methodology for self-adaptive software in CPS, leveraging simulation and IIoT to enable real-time system reconfiguration. These papers demonstrate different yet complementary approaches to integrating HDTs into manufacturing processes. Additionally, several works emphasize the role of HDTs in enhancing the human-centric manufacturing environment. For example, ref. [8] develops an IIoT-based thermal imaging system that uses edge computing and smart sensors for real-time hazard detection, while [63] proposes an MBD-enabled DT method for small-batch assembly processes, designed to reduce cognitive burdens on workers through simulation and BDA integration. Both highlight practical implementations of HDTs for improving workplace safety and adaptability. Lastly, advancements in bi-directional human–machine interaction, a hallmark of I5.0, are addressed in studies such as [54], which present a model for bi-directional data transmission in human-centered CPS. This study emphasizes H/V system integration, showcasing how connecting hierarchical and functional levels within manufacturing systems improves communication and functionality. In parallel, ref. [55] identifies technical and organizational challenges in applying DT technologies. It underscores the importance of H/V integration in enabling seamless interoperability between physical and virtual components, particularly through IIoT and simulation, as shown in Table 2. Across these studies, simulation, IIoT, and BDA emerge as pivotal technologies, consistently enabling real-time data collection, analysis, and decision-making, with targeted applications ranging from hazard prevention to adaptive system design. Together, these works provide a comprehensive view of how HDTs, supported by cutting-edge technologies, are reshaping industrial landscapes, blending human-centric principles with advanced digital tools to foster safety, flexibility, and innovation in manufacturing systems.
4.2.2. Human–Machine Collaboration
The Human–machine collaboration category encompasses six studies that explore the integration of humans and machines in manufacturing processes, which is a cornerstone of the I5.0 paradigm. By enhancing human–machine collaboration, HDTs promote social sustainability by ensuring that human roles are supported rather than replaced. This enables continuous skill development, fair task distribution, and job enrichment through collaborative robotics. This human-centric approach redefines traditional production models by promoting flexibility and intelligent manufacturing systems that adapt dynamically to fluctuating tasks. The synergy between human insights and robotic capabilities is enabled by HDTs, which facilitate real-time monitoring, simulation, and optimization of both human and machine parameters to ensure seamless integration and operational efficiency [71]. This study emphasizes dynamic task allocation between humans and robots, which inherently requires H/V system integration to ensure seamless communication and operational coherence across functional and hierarchical levels. In such collaborative systems, the roles of human operators and machines are complementary, with tasks assigned based on their specific capabilities. HDTs enhance this collaboration by dynamically assessing these characteristics and optimizing task distribution in real time. The studies in this category emphasize the importance of reconfigurable manufacturing systems, particularly in human-robot collaboration scenarios. These systems integrate components such as machine tools, robots, and operators, working in unison to complete tasks effectively. The inclusion of technologies such as Augmented Reality (AR) and Autonomous Robots, alongside simulation, IIoT, and BDA, strengthens these systems’ adaptability. AR, through its integration with AI, provides operators with immersive, real-time interfaces that enhance decision-making and productivity in manufacturing environments [41]. Autonomous Robots complement this by handling tasks that are unsafe or repetitive for humans, thereby ensuring both safety and efficiency while maintaining collaborative functionality [71]. Frameworks proposed in [60] introduce a novel approach to integrating DTs, VR, and IIoT within human-centered industrial metaverse applications, emphasizing collaborative and training functionalities. Similarly, ref. [42] proposes a holistic DT framework that embraces the full manufacturing process, employing H/V system integration to enhance system-level optimization and bridge the gap between human behavior modeling and machine capabilities. These frameworks expand the role of DTs by creating interconnected systems that bridge human and machine capabilities. Meanwhile, ref. [46] highlights the use of H/V system integration alongside simulation and IIoT to create resilient and adaptive assembly lines that support dynamic task allocation. At the same time, [12] presents an approach for advancing effective collaboration using DT technology. Similarly, ref. [71] highlights a methodology for dynamic task allocation in human-robot collaborative manufacturing, emphasizing technologies such as simulation and AR for process optimization. From this analysis, simulation emerges as the most widely used technology, serving as a core enabler for modeling and replicating processes, while AR and Autonomous Robots significantly enhance human–machine interaction, as shown in Table 3. The convergence of IIoT and Big Data also plays a crucial role in enabling data-driven decision-making and real-time system adaptability. Collectively, these contributions illustrate the critical role of HDTs and related technologies in advancing human–machine collaboration, enabling I5.0 to bridge the gap between human-centric and technology-driven innovation.
4.2.3. Human Behavior
Human behavior represents a crucial dimension in the development of HDTs, focusing on integrating workers’ activities, skills, and decision-making into DT systems. This category encompasses nine studies, all of which aim to model and simulate human behavior to enhance the adaptability, productivity, and resilience of manufacturing systems. Incorporating human behavior into DTs ensures that these systems dynamically respond to the competencies and needs of workers, aligning with the human-centric principles of I5.0. The consideration of human behavior in HDTs directly aligns with social sustainability principles by supporting worker engagement, reducing cognitive overload, and ensuring tailored work experiences that respect individual differences. Several contributions highlight practical methodologies and frameworks for embedding human behavior into HDTs. Ref. [30] introduces a methodology to digitize operator skills, enabling the optimization of job rotation and performance management within semi-automatic production lines. Similarly, ref. [62] proposes an intelligent maintenance support system, leveraging historical maintenance data to support decision-making and improve system adaptability to workers’ conditions. Ref. [47] extends DT capabilities by integrating human characterization into the Asset Administration Shell (AAS), enhancing operator well-being and resilience through real-time human data integration. Complementing these, ref. [51] proposes a framework to incorporate human factors into DT-based joint production and maintenance scheduling, focusing on improving safety, productivity, and worker well-being. Conceptual studies delve deeper into exploring human behavior within HDTs. For example, ref. [52] develops an HDT model that integrates human needs and decision-making processes into organizational systems, showcasing how HDTs can support adaptive and human-centered work environments. Ref. [61] emphasizes human–machine harmonization by proposing a framework that ensures real-time interaction and integration of human behavior in manufacturing systems, enabling systems to adapt dynamically to workers’ contributions. Meanwhile, ref. [70] explores the feasibility of HDTs for lifecycle health management, proposing a system architecture to incorporate human factors into both physical and digital domains. Technological innovations play a significant role across these studies. Ref. [44] emphasizes the use of IIoT and smart sensors in human-centered manufacturing, particularly for SMEs, to collect and analyze real-time behavioral data. Ref. [48] utilizes VR to generate auto-labeled datasets, creating simulations for human action recognition in the context of human-robot collaboration. This integration of IIoT and VR underscores their pivotal role in enabling accurate simulations and behavioral analysis within HDTs. Overall, the most commonly employed technologies across this category include IIoT, VR, and data analytics, reflecting their importance in collecting, simulating, and integrating human behavior into DT systems, as shown in Table 4. These technologies not only enable real-time monitoring and decision-making but also ensure that manufacturing systems are adaptable to workers’ diverse needs, aligning with the human-centric values of I5.0. By addressing human behavior, these studies demonstrate the transformative potential of HDTs in creating safer, more efficient, and resilient industrial environments.
4.2.4. Ergonomics and Safety
The Ergonomics and Safety category includes twelve studies, making it the most extensive in this research, as shown in Table 5. This domain underscores the essential role of safety management and ergonomics in advancing human-centric manufacturing systems, which aligns with the core principles of I5.0. Focusing on ergonomics and safety, HDTs address social sustainability by reducing workplace injuries, enhancing worker comfort, and fostering equitable working conditions through adaptive industrial designs. HDTs serve as a foundational technology in this field, leveraging real-time data, advanced simulations, and immersive technologies to enhance operator safety and optimize ergonomic conditions [69]. Ergonomics, which focuses on improving human-system interactions to promote well-being and performance, becomes critical when addressing risks such as musculoskeletal disorders in industrial settings. HDTs provide a sophisticated solution for mitigating such risks by simulating human interactions with machines and environments, ensuring processes align with the physical and cognitive capacities of workers [56]. Several studies illustrate innovative approaches to integrating HDTs with ergonomic and safety frameworks. Ref. [33] proposes an HDT system designed to enhance safety management and work organization through real-time monitoring of worker activities. Similarly, ref. [56] introduces a methodology to embed human factors data into HDTs, facilitating real-time task scheduling and ergonomic enhancements. Technologies such as VR and AR are pivotal in this domain, enabling immersive simulations for training and analysis, as highlighted by [57], which explores VR-based interfaces to improve robot manipulation and operator interaction. Ref. [53] highlights the role of multi-sensory HMIs in enabling human-centric HDTs, leveraging 6G technologies to enhance real-time feedback and system adaptability for workers. Beyond safety, several studies focus on the role of ergonomics in enhancing productivity and well-being. Refs. [13,45,64] propose frameworks and roadmaps that integrate physical and virtual representations of workers into manufacturing systems, ensuring ergonomic principles are embedded in system design. For instance, ref. [64] outlines an Ergonomics 4.0 framework that combines DHM with I4.0 tools, bridging the gap between human-centered design and technological advancements. Similarly, ref. [66] develops an Operator 4.0 monitoring system that collects performance and ergonomic data, enabling smart factories to adapt workflows to workers’ needs. Ref. [26] provides a comprehensive review of enabling technologies and development strategies for HDTs, emphasizing their potential in improving both safety and ergonomics through advanced frameworks and guidelines. Safety management systems also feature prominently in this category. Ref. [43] presents a real-time monitoring architecture using HDTs to improve worker safety and system resilience, while [69] introduces a semantic reasoning method to address safety challenges in human-centered manufacturing. Moreover, ref. [68] integrates DTs with XR to enable scalable applications, enhancing safety management through immersive training environments. Together, these contributions highlight the increasing sophistication of HDTs in reducing occupational risks and ensuring compliance with human-centric principles. Across these studies, key enabling technologies such as IIoT, AR, VR, and semantic reasoning systems emerge as central to advancing ergonomics and safety. IIoT plays a significant role in collecting real-time data on worker behavior and environmental conditions, while AR and VR enhance training and human–machine interaction. These technologies, in tandem with the capabilities of HDTs, enable manufacturing systems to adapt dynamically to worker needs, ensuring safety and ergonomic considerations are prioritized. Collectively, these works demonstrate the transformative potential of HDTs in fostering safer, more adaptable, and human-centric industrial environments, aligning with the broader objectives of I5.0.
4.2.5. Human Digital Representation
The category of Human digital representation focuses on the integration of psychological and physiological aspects of human well-being into HDTs, as shown in Table 6. Unlike categories that emphasize task optimization or skill development, this domain addresses the holistic representation of mental and physical health within the virtual environment of DTs. The development of Human Digital Twins ensures that social sustainability goals are integrated into manufacturing by digitally representing and addressing workers’ physiological and psychological needs, thus promoting well-being and reducing inequality. The literature on this topic distinguishes between two main aspects: psychological representation and physiological representation [35], both of which are critical for advancing human-centricity in I5.0 systems. The Psychological health subcategory focuses on the mental health and cognitive well-being of workers. Among the six papers in this domain, four emphasize the integration of psychological factors into HDT architectures. For instance, ref. [35] introduces a conceptual model of HDTs designed to incorporate psychological elements, while [65] reviews the evolution of human digital representation in manufacturing, highlighting the role of synchronization and mental well-being. Psychological factors such as fatigue, concentration, stress, and emotional states (e.g., anxiety and depression) are shown to significantly affect productivity, safety, and decision-making. Ref. [21] proposes an AI-based HDT model aimed at enhancing human-robot interaction by prioritizing safety, reliability, and mental health considerations. Similarly, ref. [24] provides a systematic review of advancements and challenges in integrating I5.0 principles into human-centric smart manufacturing, focusing specifically on how HDTs can address mental well-being and enhance human-system interactions. By embedding these psychological factors into HDTs, the systems enable real-time monitoring and adaptive responses to support worker well-being and enhance operational efficiency. The selected studies underscore the necessity of addressing mental health in dynamic industrial environments to maintain system adaptability and worker productivity. The Physiological health subcategory is increasingly gaining traction, particularly in healthcare applications. Two papers explore this dimension, emphasizing the transformative potential of HDTs in individualized precision medicine and patient care. Ref. [67] discusses how HDTs have revolutionized healthcare by enabling patient-specific simulations, improving diagnostics, and optimizing treatment plans. Similarly, ref. [59] introduces the concept of perioperative HDTs, focusing on digital biomarkers and AI-driven care to enhance surgical outcomes. These studies illustrate the applicability of HDTs in healthcare, demonstrating their ability to simulate physiological health attributes and support critical medical decision-making processes. For instance, during the COVID-19 pandemic, HDTs facilitated personalized care strategies, exemplified by models developed for aneurysm treatment and cardiac surgery simulations. Despite being in their early stages, these applications highlight the adaptability of HDTs in addressing complex healthcare challenges. Technologically, simulation and IIoT are the most frequently utilized tools in this category, consistent with other HDT domains. These technologies enable real-time data collection, monitoring, and dynamic adjustments, providing the foundation for both psychological and physiological representations. While the healthcare applications of HDTs are relatively new compared to manufacturing, the potential for expansion into diverse industries remains significant. The integration of these dimensions into HDTs reinforces the human-centric approach of I5.0 and demonstrates the versatility of these systems in prioritizing human well-being. Collectively, the studies in this category reveal the transformative potential of HDTs in creating adaptive, health-focused systems that extend beyond industrial applications to address broader societal needs.
5. Discussion
It is valuable to analyze how the peculiarities of the categories have developed in relation to the analytical dimensions outlined in Table 7. The findings underscore how HDTs serve as a bridge between I4.0’s technology-driven focus and I5.0’s human-centric principles by integrating enabling technologies with human-centric dimensions such as ergonomics, human–machine collaboration, and digital representation. Social sustainability emerges as a fundamental pillar driving HDT development, ensuring that technological advancements extend beyond productivity goals to promote human well-being, equity, and empowerment across all five domains outlined in this review. The distribution of article types reveals significant trends in how research approaches address these domains. HDT and Industry 5.0 is predominantly explored through reviews (3) and methodologies (2), emphasizing a balance between conceptual exploration and actionable implementation strategies. This aligns with the emerging nature of I5.0, which demands foundational analysis and practical guidelines for bridging theory and application. The underrepresentation of Frameworks (1) in this domain, however, suggests a lack of structured models for applying I5.0 principles with HDTs. Conversely, Human–machine collaboration relies heavily on Frameworks (4), highlighting the need for structured solutions to manage human–machine interactions. The limited presence of methodologies (1) indicates a gap in task-specific applications, which future research must address. Human behavior demonstrates a more balanced representation, with a significant number of Frameworks (3) and methodologies (4). This reflects an ongoing effort to integrate human factors conceptually and practically. However, the single Guideline suggests limited standardization in managing behavioral data within HDT systems. Ergonomics and Safety exhibits the highest number of frameworks (5) and methodologies (4), demonstrating a mature focus on both conceptual models and practical applications for worker safety and ergonomic risk management. The relatively fewer reviews (2) imply that this domain has moved beyond foundational exploration toward implementation. Finally, Human digital representation shows a strong emphasis on reviews (3) and models (2), reflecting its focus on exploring psychological and physiological aspects. The absence of methodologies in this domain highlights a gap in practical applications for operationalizing these representations in industrial settings. The distribution of I4.0 technologies also reveals domain-specific trends. Simulation (41) and IIoT (30) dominate across all domains, underscoring their foundational role in real-time monitoring, data collection, and predictive analytics. However, these technologies’ integration with human-centered concepts such as human–machine collaboration and human digital representation illustrates how HDTs merge technological and human dimensions, fulfilling I5.0’s vision of socially sustainable manufacturing systems. HDT and Industry 5.0 relies most heavily on simulation (8), IIoT (5), and BDA (6), reflecting a focus on process modeling and data-driven insights for achieving I5.0 goals. In contrast, Human–machine collaboration emphasizes AR (4) and Autonomous Robots (3) alongside simulation (6), highlighting their role in creating immersive, collaborative environments. Human behavior similarly integrates simulation (9), BDA (7), and IIoT (7), indicating the centrality of data-driven technologies in modeling human factors. Ergonomics and Safety shows the broadest technological adoption, with the highest reliance on simulation (12), BDA (11), and AR (10), enabling precise risk assessment and mitigation. Human digital representation also emphasizes simulation (6), BDA (5), and AR (4), particularly in modeling health-related factors. However, horizontal/vertical system integration (6) and cybersecurity (3) remain underexplored across domains, suggesting vulnerabilities in interconnected operations and data security. The industry focus analysis highlights the dominance of General manufacturing (24) across all domains, reflecting the versatility of HDTs in broad industrial applications. However, sector-specific studies are concentrated in aerospace (3), automotive (3), and healthcare (4).
Building on the data presented in the previous table, a conceptual framework (Table 8) is proposed to illustrate the key domains that make up the HDTs.
The conceptual framework reveals a rich and multifaceted ecosystem of research on HDTs, marked by clear strengths and notable gaps that provide opportunities for future exploration. The framework also illustrates that HDTs not only implement I4.0 technologies but also advance toward I5.0 by embedding human values such as well-being, adaptability, and social responsibility into digital ecosystems. This duality supports the argument that HDTs bridge the operational efficiency of I4.0 with the human-centric imperatives of I5.0. Each domain reflects distinct aspects of social sustainability: HDTs and Industry 5.0 emphasizes equitable access to enabling technologies; Human–machine collaboration focuses on fair task distribution and shared decision-making; Human behavior addresses cognitive and emotional well-being; Ergonomics and Safety ensure safe and health-supportive work environments; and Human digital representation highlights personalized work experiences and skill development. Together, these facets contribute to a comprehensive, human-centric approach that aligns technological progress with social responsibility. The cross-domain trends underscore several insights. Simulation, IIoT, and BDA emerge as universally adopted technologies, forming the backbone of HDT capabilities across all domains. AR and Autonomous Robots, however, are more domain-specific, playing pivotal roles in Human–machine collaboration and Ergonomics and Safety, respectively. The concentration of research in manufacturing underscores the broad applicability of HDTs across diverse industrial settings, while the sector-specific focus on healthcare and automotive highlights promising opportunities for tailored innovations in these domains. However, notable gaps remain, particularly in industries with strong craftsmanship elements, such as the fashion sector, which has yet to be thoroughly explored for potential HDT applications. Similarly, the underutilization of H/V system integration and cybersecurity highlights areas for improvement in creating cohesive, secure ecosystems. Addressing these gaps will be crucial for advancing HDTs’ transformative potential across industries and domains. Frameworks are heavily represented in domains such as Human–machine collaboration and Ergonomics and Safety, underscoring the need for structured conceptual models to address complex problems. The strong presence of these frameworks demonstrates the integration of human cognitive and physical capabilities into industrial system design—a defining element of I5.0’s human-centric paradigm facilitated by HDTs. However, the scarcity of corresponding methodologies points to a significant limitation in translating these frameworks into actionable tools. This gap highlights a critical challenge in advancing from I4.0’s technology-driven approach to I5.0’s human-centric vision. While digital monitoring technologies such as simulation, IIoT, and BDA are well-established, the integration of human-centric dimensions such as cognitive, psychological, and physiological data modeling remains limited. Bridging this gap is essential for fully leveraging HDTs as a transformative framework that connects technological efficiency with human-centered innovation. This dichotomy between conceptualization and application is most pronounced in human digital representation. While models focus on psychological and physiological aspects, the lack of practical methodologies hampers the integration of these representations into industrial processes. This gap reflects a broader challenge in the field—bridging theoretical insights with operational implementation. The concentration of research in high-technology sectors, including aerospace, automotive, and healthcare, emphasizes the alignment of HDTs with industries that have advanced technological infrastructures. However, the absence of studies in craftsmanship-oriented industries, such as fashion, represents a missed opportunity. These sectors, though less digitalized, could significantly benefit from customized HDT applications that monitor and enhance manual dexterity, creativity, and unique human-centric processes. Expanding HDTs into these underrepresented industries would not only diversify their applicability but also unlock new dimensions of innovation. General manufacturing dominates as the primary sector of application, reflecting the broad versatility of HDTs. However, this emphasis on general contexts appears to limit the development of highly specialized, vertical solutions. Emerging industries such as hydropower and less-studied applications in healthcare reveal untapped potential for expanding HDT research into areas such as sustainable energy management and personalized medical care. These opportunities underscore the need for a more nuanced approach that balances generalizability with sector-specific innovation. The role of AR and autonomous robotics emerges as highly specialized within specific domains, particularly human–machine collaboration and ergonomics and safety. While their contributions are substantial, the restricted scope of their application limits their transformative potential in domains such as human behavior and digital representation. Expanding these technologies to facilitate immersive and automated solutions in underrepresented areas could catalyze new breakthroughs in HDT research and implementation. Overall, the conceptual framework highlights an active research landscape striving to balance foundational analysis with practical implementation. The strong presence of frameworks and models indicates an ongoing effort to establish robust theoretical foundations, while the relatively low number of methodologies points to an unmet need for operational maturity. Addressing this gap, particularly in areas such as horizontal/vertical integration, offers a pathway to creating more cohesive and interoperable systems. Similarly, extending the reach of HDTs into less-digitalized industries and emphasizing complementary technologies would significantly enhance their scope and impact. By adopting a more integrated and exploratory approach, future research can expand the transformative potential of HDTs across diverse industrial and human-centric contexts.
6. Conclusions
This research has offered a systemic analysis with the aim of understanding the HDTs in the context of I4.0/I5.0. To do so, a systematic literature review, leading to a descriptive and thematic analysis, has been performed to structure the knowledge regarding the embracement and implementation of the HDTs concept, enabled by the joint adoption of DTs technologies and I5.0 human-centric principles. Articles were investigated, leading to the detection of five thematic categories in the HDTs domain (HDTs and Industry 5.0, Human-machine collaboration, Human behavior, Ergonomics and Safety, Human digital representation). These five categories were also useful to build a framework able to describe the single dimensions of the HDTs research domain and to gradually raise companies’ managers awareness to successfully approach this concept. The study provides a structured lens to navigate the multidimensionality of HDTs, emphasizing their relevance as pivotal enablers of the I5.0 paradigm. The findings of this review provide a comprehensive analysis of the HDT research landscape, highlighting the peculiarities of each identified domain and their intersections with I4.0 technologies, methodological approaches, and industry applications. The dominance of simulation, IIoT, and BDA across all domains underscores their critical role in creating the foundational infrastructure of HDTs. These technologies are pivotal in collecting, processing, and analyzing human-centric data, enabling real-time insights and adaptive decision-making. Their prevalence reflects the central objective of HDTs: to integrate and enhance human capabilities in increasingly complex and dynamic industrial systems. Interestingly, the focus on these technologies mirrors their maturity and proven impact, while the underrepresentation of others, such as horizontal/vertical system integration and cybersecurity, may indicate barriers related to their complexity or nascent development within HDT-specific contexts. Addressing these gaps could catalyze more cohesive and secure HDT ecosystems. The transition from I4.0 to I5.0 depends on the centrality of human values, and HDTs are at the heart of this shift. By embedding psychological, physiological, and behavioral dimensions into digital systems, HDTs provide a means to bridge the gap between human-centric design and technological advancement. These human aspects are critical: they allow industries to account for mental health, physical well-being, and individual capabilities, ensuring that technological innovation aligns with the diverse needs of human operators. The integration of these aspects enhances both operational efficiency and human satisfaction, reinforcing the dual focus of I5.0 on technological and societal progress. Industry applications further illustrate the transformative potential of HDTs, with early adoption in aerospace, automotive, and healthcare. These sectors, characterized by their complexity and reliance on precision, have capitalized on HDTs to optimize workflows, enhance safety, and enable personalized solutions. Yet, traditional industries such as fashion, craftsmanship, or agri-food remain largely unexplored. This represents a significant limitation of current research, as these sectors could offer valuable insights into the application of HDTs for preserving manual skills, fostering creativity, and advancing sustainable practices. Future investigations into these domains could redefine the boundaries of HDT applications. The findings also reveal a methodological gap. While frameworks and conceptual models dominate the literature, practical methodologies for operationalizing HDTs remain scarce. This disconnect between theory and practice underscores the need for tools and metrics that facilitate the seamless integration of human-centric factors into digital ecosystems. Without such advancements, industries risk underutilizing HDTs’ potential to drive innovation and inclusivity. In conclusion, this research highlights both the promise and challenges of HDTs as enablers of the I4.0 to I5.0 transition. Future research must address critical gaps by developing actionable methodologies, exploring underrepresented sectors, and fostering deeper integration of complementary technologies such as AR and autonomous robotics. Theoretical contributions should continue to refine the HDT framework, while practical efforts must focus on delivering scalable, sector-specific solutions that advance human-centricity across diverse industries. The contribution of this paper is twofold: theoretical and practical. Theoretically, it synthesizes extant literature, structuring it into clear domains and analytical dimensions, providing a roadmap for future research. Practically, the insights presented herein serve as managerial guidelines for industries seeking to integrate HDTs into their processes. Managers can use the findings to identify enabling technologies, understand industry-specific applications, and address challenges such as interoperability and organizational readiness. By bridging these theoretical and practical dimensions, this study lays a foundation for more inclusive and impactful applications of HDTs in shaping the future of human-centric industries.
Given that I5.0 is still an emerging concept, this review faces certain limitations. The limited availability of peer-reviewed articles explicitly addressing HDTs within the I5.0 context highlights the early stage of academic research. Diverse interpretations of enabling technologies, training methods, and architectural frameworks result in fragmented insights. Despite these challenges, this review aimed to synthesize the most relevant references and extract key insights through a systematic analysis process. Efforts were made to balance conceptual exploration with technological perspectives, minimizing the impact of the identified limitations. To advance research, longitudinal studies and real-world applications should validate HDT implementations across industries, bridging theory and practice. Additionally, addressing data privacy and ethical concerns is crucial for transparent management in HDTs, while expanding research into underexplored sectors such as fashion would enhance HDTs applicability beyond high-tech industries.
Conceptualization, I.B., V.F. and R.B.; methodology, I.B. and V.F.; writing—original draft preparation, I.B. and V.F.; writing—review and editing, I.B., V.F. and R.B.; visualization, I.B. and V.F.; supervision, R.B. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
No new data were created or analyzed in this study. Data sharing is not applicable to this article.
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
Footnotes
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![View Image - Figure 1. Search Strategy (adapted by [37]).](https://media.proquest.com/media/hms/THUM/2/FiM9b?_a=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%3D%3D&_s=gYgAVE3WyOWkQp8ovWq59obXiOY%3D "View Image - Figure 1. Search Strategy (adapted by [37]).")
![View Image - Figure 6. HDTs domains’ distribution (adapted by [37]).Adel (2022): [25]Coronado et al. (2024): [46]. Naqvi et al. (2022): [62] Peruzzini et al. (2020): [66] Perez et al. (2022): [65] Erol et al. (2020): [67].](https://media.proquest.com/media/hms/THUM/12/FiM9b?_a=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%3D%3D&_s=bzYUM3pSipUe0k87s%2FccfbC3rZk%3D "View Image - Figure 6. HDTs domains’ distribution (adapted by [37]).Adel (2022): [25]Coronado et al. (2024): [46]. Naqvi et al. (2022): [62] Peruzzini et al. (2020): [66] Perez et al. (2022): [65] Erol et al. (2020): [67].")
Enlarge this image.Figure 6. HDTs domains’ distribution (adapted by [37]).Adel (2022): [25]Coronado et al. (2024): [46]. Naqvi et al. (2022): [62] Peruzzini et al. (2020): [66] Perez et al. (2022): [65] Erol et al. (2020): [67].
HDT-related studies.
| Ref. | Type | Main Objective | Category |
|---|---|---|---|
| [ | Review | Discusses I5.0 opportunities, challenges, and prospects. Focuses on human–machine collaboration, emerging technologies, and their applications. | HDT and |
| [ | Framework | Proposes a hierarchical framework for digital triplets integrating human intuition, knowledge, and creativity into cyberspace, enhancing human–machine interaction. | Human- |
| [ | Review | Reviews enabling technologies for HDTs and provides guidelines for their development and application. | Ergonomics and Safety |
| [ | Framework | Introduces a holistic DT approach integrating human behavior and full manufacturing process dependencies to improve resilience and optimization. | Human– |
| [ | Framework | Proposes a real-time monitoring system architecture using HDTs to improve worker safety and system resilience. | Ergonomics and Safety |
| [ | Guideline | Provides recommendations for using IIoT and smart sensors to support human-centered manufacturing, particularly in SMEs. | Human |
| [ | Framework | Proposes a five-level roadmap for developing Human Body DTs for healthcare applications, addressing ethical and technical challenges. | Ergonomics and Safety |
| [ | Framework | Proposes bridging NEP (a human-centered development framework intended to assist users and developers with diverse backgrounds and resources in constructing interactive human–machine systems) + and Robot Operating System (ROS) frameworks for developing human-centered systems in I5.0 applications. | Human- |
| [ | Methodology | Extends DTs by integrating human characterization into Asset Administration Shell for improved operator well-being and resilience. | Human |
| [ | Methodology | Develops a method for generating auto-labeled datasets using DTs and Virtual Reality (VR) for human action recognition in human-robot collaboration. | Human |
| [ | Framework | Proposes an operator-centric DT architecture for composites production, emphasizing decision-making support. | HDT and |
| [ | Methodology | Proposes self-adaptive software for CPSs using DTs to manage resilience and enable dynamic reconfiguration. | HDT and |
| [ | Guideline | Describes a cost-effective IIoT thermal imaging system for enhancing safety in human-centered manufacturing. | HDT and |
| [ | Framework | Proposes a framework integrating human factors into DT-based scheduling to improve safety, well-being, and productivity. | Human |
| [ | Model | Develops an HDT model to integrate human needs and decision-making into organizational environments. | Human |
| [ | Review | Explores the role of multi-sensory Human–Machine Interfaces (HMIs) in enabling human-centric DTs within the 6G industrial revolution. | Ergonomics and Safety |
| [ | Framework | Proposes a unified HDT framework integrating physical and virtual twins to advance ergonomic analysis and real-time monitoring. | Ergonomics and Safety |
| [ | Model | Proposes a model for bi-directional data transmission in human-centered Cyber-Physical Systems (CPS) for enhanced DT functionality. | HDT and |
| [ | Model | Proposes an HDT system to improve worker safety and work management through real-time analysis. | Ergonomics and Safety |
| [ | Review | Identifies technical, organizational, and methodological challenges in DT applications in manufacturing and proposes measures to enhance their effectiveness. | HDT and |
| [ | Methodology | Develops a methodology to incorporate human factors data into DTs, enabling real-time task scheduling and ergonomic improvements. | Ergonomics and Safety |
| [ | Review | Analyzes enabling technologies and methods for human-centric DTs, emphasizing human–machine collaboration in I5.0. | Human– |
| [ | Methodology | Explores the use of VR-based interfaces in DTs to improve robot manipulation validation and user interaction in collaborative systems. | Ergonomics and Safety |
| [ | Methodology | Introduces a methodology to digitize operator skills for integration into DTs, improving job rotation and performance management. | Human |
| [ | Review | Conducts a bibliometric analysis of DT applications in supply chains, highlighting trends and integration of AI and human-centric systems. | HDT and |
| [ | Framework | Explores the concept of perioperative HDTs for individualized precision medicine, focusing on digital biomarkers and AI-driven care. | Human digital representation |
| [ | Framework | Proposes a framework integrating DTs, VR, and IIoT into a human-centered industrial metaverse for collaboration and training. | Human– |
| [ | Framework | Develops a DT framework emphasizing human–machine harmonization and real-time interaction to enhance manufacturing systems. | Human |
| [ | Methodology | Proposes an intelligent maintenance support system leveraging past maintenance data and DT technology for smart manufacturing. | Human |
| [ | Model | Introduces the concept and preliminary model of HDTs to integrate human elements into I4.0 systems. | Human digital representation |
| [ | Methodology | Develops a Model-Based Definition (MBD) enabled DT modeling method to assist cognition in manual assembly processes for small-batch manufacturing. | HDT and |
| [ | Framework | Proposes a framework for Ergonomics 4.0, integrating Digital Human Modelling (DHM) with I4.0 concepts for improved ergonomics. | Ergonomics and Safety |
| [ | Review | Reviews the evolution of human representation in manufacturing systems, focusing on synchronization and well-being in advanced environments. | Human digital representation |
| [ | Framework | Develops a framework for integrating human factors into smart factories, using monitoring systems to improve ergonomics and performance. | Ergonomics and Safety |
| [ | Model | Proposes an AI-based model to enhance human-robot interaction, prioritizing safety, reliability, and human-centered design principles. | Human digital representation |
| [ | Review | Explores the transformative potential of DTs in healthcare for personalized medicine, diagnostics, and treatment planning. | Human digital representation |
| [ | Methodology | Introduces a method integrating DTs and XR for scalable industrial applications and system interoperation. | Ergonomics and Safety |
| [ | Methodology | Proposes a semantic reasoning method using DTs for addressing safety challenges in human-centered manufacturing. | Ergonomics and Safety |
| [ | Framework | Explores the concept and feasibility of HDTs for lifecycle health management, proposing a system architecture and implementation approach. | Human |
| [ | Review | Reviews advancements and challenges in human-centric smart manufacturing, focusing on the integration of I5.0 principles. | Human digital representation |
| [ | Methodology | Proposes a method for dynamic task allocation between humans and robots to optimize production efficiency in intelligent manufacturing systems. | Human– |
HDT and Industry 5.0 domain analysis within I4.0 technologies.
| I4.0 Technologies | [ | [ | [ | [ | [ | [ | [ | [ | Tot |
|---|---|---|---|---|---|---|---|---|---|
| Additive manufacturing | - | ||||||||
| AR | - | ||||||||
| Autonomous robots | x | 1 | |||||||
| BDA | x | x | x | x | x | x | 6 | ||
| Cybersecurity | x | 1 | |||||||
| H/V system integration | x | x | x | 3 | |||||
| Simulation | x | x | x | x | x | x | x | x | 8 |
| The cloud | x | x | 2 | ||||||
| The IIoT | x | x | x | x | x | 5 |
Human–machine collaboration domain analysis within I4.0 technologies.
| I4.0 Technologies | [ | [ | [ | [ | [ | [ | Tot |
|---|---|---|---|---|---|---|---|
| Additive manufacturing | - | ||||||
| AR | x | x | x | x | 4 | ||
| Autonomous robots | x | x | x | 3 | |||
| BDA | x | x | x | x | 4 | ||
| Cybersecurity | x | 1 | |||||
| H/V system integration | x | x | x | 3 | |||
| Simulation | x | x | x | x | x | x | 6 |
| The cloud | - | ||||||
| The IIoT | x | x | x | x | x | 5 |
Human behavior domain analysis within I4.0 technologies.
| I4.0 Technologies | [ | [ | [ | [ | [ | [ | [ | [ | [ | Tot |
|---|---|---|---|---|---|---|---|---|---|---|
| Additive manufacturing | - | |||||||||
| AR | x | x | x | x | 4 | |||||
| Autonomous robots | x | x | x | 3 | ||||||
| BDA | x | x | x | x | x | x | x | 7 | ||
| Cybersecurity | - | |||||||||
| H/V system integration | - | |||||||||
| Simulation | x | x | x | x | x | x | x | x | x | 9 |
| The cloud | x | 1 | ||||||||
| The IIoT | x | x | x | x | x | x | x | 7 |
Ergonomics and safety domain analysis within I4.0 technologies.
| I4.0 Technologies | [ | [ | [ | [ | [ | [ | [ | [ | [ | [ | [ | [ | Tot |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Additive manufacturing | - | ||||||||||||
| AR | x | x | x | x | x | x | x | x | x | x | 10 | ||
| Autonomous robots | x | x | x | x | x | 5 | |||||||
| BDA | x | x | x | x | x | x | x | x | x | x | x | 11 | |
| Cybersecurity | x | 1 | |||||||||||
| H/V system integration | - | ||||||||||||
| Simulation | x | x | x | x | x | x | x | x | x | x | x | x | 12 |
| The cloud | x | x | x | 3 | |||||||||
| The IIoT | x | x | x | x | x | x | x | x | x | 9 |
Human digital representation domain analysis within I4.0 technologies.
| Psychological Health | Physiological Health | ||||||
|---|---|---|---|---|---|---|---|
| I4.0 Technologies | [ | [ | [ | [ | [ | [ | Tot |
| Additive manufacturing | - | ||||||
| AR | x | x | x | x | 4 | ||
| Autonomous robots | x | x | 2 | ||||
| BDA | x | x | x | x | x | 5 | |
| Cybersecurity | - | ||||||
| H/V system integration | - | ||||||
| Simulation | x | x | x | x | x | x | 6 |
| The cloud | x | 1 | |||||
| The IIoT | x | x | x | x | x | 5 | |
Articles’ diffusion between the three dimensions (Type, I4.0 Technologies, and Industry) and five domains.
| HDT and | Human- | Human | Ergonomics and Safety | Human | Tot | |
|---|---|---|---|---|---|---|
| Type | ||||||
| Framework | 1 | 4 | 3 | 5 | 1 | 14 |
| Guideline | 1 | - | 1 | - | - | 2 |
| Methodology | 2 | 1 | 4 | 4 | - | 11 |
| Model | 1 | - | 1 | 1 | 2 | 5 |
| Review | 3 | 1 | - | 2 | 3 | 9 |
| I4.0 Technologies | ||||||
| Additive | - | - | - | - | - | 0 |
| AR | - | 4 | 4 | 10 | 4 | 21 |
| Autonomous | 1 | 3 | 3 | 5 | 2 | 14 |
| BDA | 6 | 4 | 7 | 11 | 5 | 32 |
| Cybersecurity | 1 | 1 | - | 1 | - | 3 |
| H/V system | 3 | 3 | - | - | - | 6 |
| Simulation | 8 | 6 | 9 | 12 | 6 | 41 |
| The cloud | 2 | - | 1 | 3 | 1 | 7 |
| The IIoT | 5 | 5 | 7 | 9 | 5 | 30 |
| Industry | ||||||
| Aerospace | 3 | - | - | - | - | 3 |
| Automotive | 2 | - | - | 1 | - | 3 |
| Healthcare | - | - | 1 | 1 | 2 | 4 |
| Hydropower | 1 | - | - | - | - | 1 |
| Manufacturing | 3 | 6 | 4 | 7 | 3 | 24 |
| Total articles | 8 | 6 | 9 | 12 | 6 | 41 |
Conceptual framework.
| Top | Relevant | Type | I4.0 | |
|---|---|---|---|---|
| HDTs and | Aerospace, | Literature discusses methodology integrating I5.0 principles into DTs, emphasizing operator decision-making [ | Review, | Simulation, IIoT, BDA |
| Human- | Manufacturing | Studies propose frameworks for immersive collaboration [ | Framework, Methodology | Simulation, IIoT, AR |
| Human | Manufacturing, Healthcare | Literature highlights methodologies for integrating IIoT and behavioral modeling [ | Methodology, Framework | Simulation, IIoT, BDA |
| Ergonomics and Safety | Manufacturing, | Literature focuses on methodologies for integrating VR/AR to monitor safety and mitigate ergonomic risks [ | Framework, Methodology | Simulation, BDA, AR |
| Human | Healthcare, | Studies address psychological and physiological HDTs for mental health and personalized care [ | Review, | Simulation, BDA, AR |
References
1. Klingenberg, C.O.; Borges, M.A.V.; Antunes, J.A.V., Jr. Industry 4.0 as a Data-Driven Paradigm: A Systematic Literature Review on Technologies. J. Manuf. Technol. Manag.; 2019; 32, pp. 570-592. [DOI: https://dx.doi.org/10.1108/JMTM-09-2018-0325]
2. Hazrat, M.A.; Hassan, N.M.S.; Chowdhury, A.A.; Rasul, M.G.; Taylor, B.A. Developing a Skilled Workforce for Future Industry Demand: The Potential of Digital Twin-Based Teaching and Learning Practices in Engineering Education. Sustainability; 2023; 15, 16433. [DOI: https://dx.doi.org/10.3390/su152316433]
3. Fani, V.; Bucci, I.; Rossi, M.; Bandinelli, R. Lean and Industry 4.0 Principles toward Industry 5.0: A Conceptual Framework and Empirical Insights from Fashion Industry. J. Manuf. Technol. Manag.; 2024; 35, pp. 122-141. [DOI: https://dx.doi.org/10.1108/JMTM-11-2023-0509]
4. Mourtzis, D.; Wang, L. 3-Industry 5.0: Perspectives, Concepts, and Technologies. Manufacturing from Industry 4.0 to Industry 5.0; Mourtzis, D. Elsevier: Amsterdam, The Netherlands, 2024; pp. 63-96. ISBN 978-0-443-13924-6
5. Ghobakhloo, M.; Iranmanesh, M.; Fathi, M.; Rejeb, A.; Foroughi, B.; Nikbin, D. Beyond Industry 4.0: A Systematic Review of Industry 5.0 Technologies and Implications for Social, Environmental and Economic Sustainability. Asia-Pac. J. Bus. Adm.; 2024; ahead-of-print [DOI: https://dx.doi.org/10.1108/APJBA-08-2023-0384]
6. Breque, M.; De Nul, L.; Petridis, A. European Commission: Directorate-General for Research and Innovation. Industry 5.0–Towards a Sustainable, Human-Centric and Resilient European Industry; Publications Office of the European Union: Luxembourg, 2021.
7. Peruzzini, M.; Prati, E.; Pelicciari, M. A Framework to Design Smart Manufacturing Systems for Industry 5.0 Based on the Human-Automation Symbiosis. Int. J. Comput. Integr. Manuf.; 2023; 37, pp. 1426-1443. [DOI: https://dx.doi.org/10.1080/0951192X.2023.2257634]
8. Fraga-Lamas, P.; Barros, D.; Lopes, S.I.; Fernández-Caramés, T.M. Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System. Sensors; 2022; 22, 8500. [DOI: https://dx.doi.org/10.3390/s22218500]
9. Fontes, C.; Carpentras, D.; Mahajan, S. Human Digital Twins Unlocking Society 5.0? Approaches, Emerging Risks and Disruptions. Ethics Inf. Technol.; 2024; 26, pp. 1-22. [DOI: https://dx.doi.org/10.1007/s10676-024-09787-1]
10. Grego, G.; Nenna, F.; Gamberini, L. Enhancing Human-Machine Interactions: A Novel Framework for AR-Based Digital Twin Systems in Industrial Environments. Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments; Crete, Greece, 26–28 June 2024; Karim, E.; Nikanfar, S.; Pavel, H.R. Association for Computing Machinery: New York, NY, USA, 2024; pp. 456-462.
11. de la Torre, A.R.; Borregan, J.; Pikatza, N.; Maria Rio, R. Integrating Human-Centric Simulations in Educational Production Lines: Advancing Ergonomics for Industry 5.0 Applications. Int. J. Prod. Manag. Eng.; 2024; 12, pp. 141-157. [DOI: https://dx.doi.org/10.4995/ijpme.2024.20919]
12. Krupas, M.; Kajati, E.; Liu, C.; Zolotova, I. Towards a Human-Centric Digital Twin for Human–Machine Collaboration: A Review on Enabling Technologies and Methods. Sensors; 2024; 24, 2232. [DOI: https://dx.doi.org/10.3390/s24072232]
13. He, Q.; Li, L.; Li, D.; Peng, T.; Zhang, X.; Cai, Y.; Zhang, X.; Tang, R. From Digital Human Modeling to Human Digital Twin: Framework and Perspectives in Human Factors. Chin. J. Mech. Eng. Engl. Ed.; 2024; 37, 9. [DOI: https://dx.doi.org/10.1186/s10033-024-00998-7]
14. Sachan, R.; Chauhan, K.; Agarwal, V. Outlook on Upskilling and Reskilling Capacities for Industry 5.0. Humanizing Businesses for a Better World of Work; Shrivastava, R.; Jain, K. Emerald Publishing Limited: Leeds, UK, 2024; pp. 1-16. ISBN 978-1-83797-333-0
15. Narkhede, G.; Pasi, B.; Rajhans, N.; Kulkarni, A. Industry 5.0 and the Future of Sustainable Manufacturing: A Systematic Literature Review. Bus. Strategy Dev.; 2023; 6, pp. 704-723. [DOI: https://dx.doi.org/10.1002/bsd2.272]
16. Ghobakhloo, M.; Mahdiraji, H.A.; Iranmanesh, M.; Jafari-Sadeghi, V. From Industry 4.0 Digital Manufacturing to Industry 5.0 Digital Society: A Roadmap Toward Human-Centric, Sustainable, and Resilient Production. Inf. Syst. Front.; 2024; [DOI: https://dx.doi.org/10.1007/s10796-024-10476-z]
17. Ghobakhloo, M.; Iranmanesh, M.; Morales, M.E.; Nilashi, M.; Amran, A. Actions and Approaches for Enabling Industry 5.0-Driven Sustainable Industrial Transformation: A Strategy Roadmap. Corp. Soc. Responsib. Environ. Manag.; 2023; 30, pp. 1473-1494. [DOI: https://dx.doi.org/10.1002/csr.2431]
18. Ruiz-de-la-Torre-Acha, A.; Guevara-Ramirez, W.; Río-Belver, R.M.; Borregan-Alvarado, J. Industry 5.0. The Road to Sustainability. Towards a Smart, Resilient and Sustainable Industry; Borgianni, Y.; Matt, D.T.; Molinaro, M.; Orzes, G. Springer Nature: Cham, Switzerland, 2023; pp. 247-257.
19. Leng, J.; Zhong, Y.; Lin, Z.; Xu, K.; Mourtzis, D.; Zhou, X.; Zheng, P.; Liu, Q.; Zhao, J.L.; Shen, W. Towards Resilience in Industry 5.0: A Decentralized Autonomous Manufacturing Paradigm. J. Manuf. Syst.; 2023; 71, pp. 95-114. [DOI: https://dx.doi.org/10.1016/j.jmsy.2023.08.023]
20. Chabane, B.; Komljenovic, D.; Abdul-Nour, G. Converging on Human-Centred Industry, Resilient Processes, and Sustainable Outcomes in Asset Management Frameworks. Environ. Syst. Decis.; 2023; 43, pp. 663-679. [DOI: https://dx.doi.org/10.1007/s10669-023-09943-w]
21. Ramírez-Gordillo, T.; Mora, H.; Pujol-Lopez, F.A.; Jimeno-Morenilla, A.; Maciá-Lillo, A. Industry 5.0: Towards Human Centered Design in Human Machine Interaction. The International Research & Innovation Forum; Springer International Publishing: Cham, Switzerland, 2024; pp. 661-672.
22. Turner, C.; Oyekan, J. Manufacturing in the Age of Human-Centric and Sustainable Industry 5.0: Application to Holonic, Flexible, Reconfigurable and Smart Manufacturing Systems. Sustainability; 2023; 15, 10169. [DOI: https://dx.doi.org/10.3390/su151310169]
23. Kokare, S.; Oliveira, J.P.; Godina, R. Life Cycle Assessment of Additive Manufacturing Processes: A Review. J. Manuf. Syst.; 2023; 68, pp. 536-559. [DOI: https://dx.doi.org/10.1016/j.jmsy.2023.05.007]
24. Zhang, C.; Wang, Z.; Zhou, G.; Chang, F.; Ma, D.; Jing, Y.; Cheng, W.; Ding, K.; Zhao, D. Towards New-Generation Human-Centric Smart Manufacturing in Industry 5.0: A Systematic Review. Adv. Eng. Inform.; 2023; 57, 102121. [DOI: https://dx.doi.org/10.1016/j.aei.2023.102121]
25. Adel, A. Future of Industry 5.0 in Society: Human-Centric Solutions, Challenges and Prospective Research Areas. J. Cloud Comput.; 2022; 11, 40. [DOI: https://dx.doi.org/10.1186/s13677-022-00314-5]
26. Asad, U.; Khan, M.; Khalid, A.; Lughmani, W.A. Human-Centric Digital Twins in Industry: A Comprehensive Review of Enabling Technologies and Implementation Strategies. Sensors; 2023; 23, 3938. [DOI: https://dx.doi.org/10.3390/s23083938]
27. Rüßmann, M.; Lorenz, M.; Gerbert, P.d.S.; Waldner, M.; Justus, J.; Engel, P.; Harnisch, M.J. Industry 4. 0: The Future of Productivity and Growth in Manufacturing Industries April 09. Boston Consult. Group; 2016; 9, pp. 54-89.
28. Galli, E.; Fani, V.; Bandinelli, R.; Lacroix, S.; Le Duigou, J.; Eynard, B.; Godart, X. Literature Review and Comparison of Digital Twin Frameworks in Manufacturing. Proc. Eur. Counc. Model. Simul. ECMS; 2023; 2023, pp. 428-434. [DOI: https://dx.doi.org/10.7148/2023-0428]
29. Corallo, A.; Del Vecchio, V.; Lezzi, M.; Morciano, P. Shop Floor Digital Twin in Smart Manufacturing: A Systematic Literature Review. Sustainability; 2021; 13, 12987. [DOI: https://dx.doi.org/10.3390/su132312987]
30. Lago Alvarez, A.; Mohammed, W.M.; Vu, T.; Ahmadi, S.; Martinez Lastra, J.L. Enhancing Digital Twins of Semi-Automatic Production Lines by Digitizing Operator Skills. Appl. Sci.; 2023; 13, 1637. [DOI: https://dx.doi.org/10.3390/app13031637]
31. Davila-Gonzalez, S.; Martin, S. Human Digital Twin in Industry 5.0: A Holistic Approach to Worker Safety and Well-Being through Advanced AI and Emotional Analytics. Sensors; 2024; 24, 655. [DOI: https://dx.doi.org/10.3390/s24020655]
32. Domínguez, L.G.I. Digital Twins in Industry 5.0—A Systematic Literatura Review. Eur. Public Soc. Innov. Rev.; 2024; 9, pp. 1-21. [DOI: https://dx.doi.org/10.31637/epsir-2024-641]
33. Kim, G.-Y.; Kim, D.; Do Noh, S.; Han, H.K.; Kim, N.G.; Kang, Y.-S.; Choi, S.H.; Go, D.H.; Song, J.; Lee, D.Y. et al. Human Digital Twin System for Operator Safety and Work Management. IFIP Advances in Information and Communication Technology; Kim, D.Y.; von Cieminski, G.; Romero, D. Springer Science and Business Media Deutschland GmbH: New York, NY, USA, 2022; Volume 664, pp. 529-536.
34. Grego, G.; Nenna, F.; Gamberini, L. XR-Based Digital Twin for Industry 5.0: A Usability and User Experience Evaluation. Proceedings of Lecture Notes in Computer Science; De Paolis, L.T.; Arpaia, P.; Sacco, M. Springer Science and Business Media Deutschland GmbH: New York, NY, USA, 2024; Volume 15027, pp. 387-403.
35. Naudet, Y.; Baudet, A.; Risse, M. Human Digital Twin in Industry 4.0: Concept and Preliminary Model. Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics; Online, 25–27 October 2021; pp. 137-144.
36. Denyer, D.; Tranfield, D. Producing a Systematic Review. The Sage Handbook of Organizational Research Methods; Sage Publications Ltd.: London, UK, 2009.
37. Smart, P.; Hemel, S.; Lettice, F.; Adams, R.; Evans, S. Pre-Paradigmatic Status of Industrial Sustainability: A Systematic Review. Int. J. Oper. Amp. Prod. Manag.; 2017; 37, pp. 1425-1450. [DOI: https://dx.doi.org/10.1108/IJOPM-02-2016-0058]
38. Ren, S.; Zhang, Y.; Liu, Y.; Sakao, T.; Huisingh, D.; Almeida, C.M.V.B. A Comprehensive Review of Big Data Analytics throughout Product Lifecycle to Support Sustainable Smart Manufacturing: A Framework, Challenges and Future Research Directions. J. Clean. Prod.; 2019; 210, pp. 1343-1365. [DOI: https://dx.doi.org/10.1016/j.jclepro.2018.11.025]
39. Maeda, J. The Laws of Simplicity; MIT Press: Cambridge, MA, USA, 2006.
40. Taddei, E.; Sassanelli, C.; Rosa, P.; Terzi, S. Circular Supply Chains in the Era of Industry 4.0: A Systematic Literature Review. Comput. Ind. Eng.; 2022; 170, 108268. [DOI: https://dx.doi.org/10.1016/j.cie.2022.108268]
41. Alimam, H.; Mazzuto, G.; Tozzi, N.; Emanuele Ciarapica, F.; Bevilacqua, M. The Resurrection of Digital Triplet: A Cognitive Pillar of Human-Machine Integration at the Dawn of Industry 5.0. J. King Saud Univ. Comput. Inform. Sci.; 2023; 35, 101846. [DOI: https://dx.doi.org/10.1016/j.jksuci.2023.101846]
42. Bécue, A.; Maia, E.; Feeken, L.; Borchers, P.; Praça, I. A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future. Appl. Sci.; 2020; 10, 4482. [DOI: https://dx.doi.org/10.3390/app10134482]
43. Berti, N.; Finco, S.; Guidolin, M.; Battini, D. Towards Human Digital Twins to Enhance Workers’ Safety and Production System Resilience. IFAC-Pap.; 2023; 56, pp. 11062-11067. [DOI: https://dx.doi.org/10.1016/j.ifacol.2023.10.809]
44. Borck, C.; Schmitt, R.; Berger, U.; Hentschel, C. IIoT and Smart Sensors in Human-Centered Manufacturing. The Future of Smart Production for SMEs: A Methodological and Practical Approach Towards Digitalization in SMEs; Springer International Publishing: Cham, Switzerland, 2022; pp. 213-224. ISBN 978-303115428-7
45. Tang, C.; Yi, W.; Occhipinti, E.; Dai, Y.; Gao, S.; Occhipinti, L. A Roadmap for the Development of Human Body Digital Twins. Nat. Rev. Electr. Eng.; 2024; 1, pp. 199-207. [DOI: https://dx.doi.org/10.1038/s44287-024-00025-w]
46. Coronado, E.; Ueshiba, T.; Ramirez-Alpizar, I.G. A Path to Industry 5.0 Digital Twins for Human–Robot Collaboration by Bridging NEP+ and ROS. Robotics; 2024; 13, 28. [DOI: https://dx.doi.org/10.3390/robotics13020028]
47. Cutrona, V.; Bonomi, N.; Montini, E.; Ruppert, T.; Delinavelli, G.; Pedrazzoli, P. Extending Factory Digital Twins through Human Characterisation in Asset Administration Shell. Int. J. Comput. Integr. Manuf.; 2023; 37, pp. 1214-1231. [DOI: https://dx.doi.org/10.1080/0951192X.2023.2278108]
48. Dallel, M.; Havard, V.; Dupuis, Y.; Baudry, D. Digital Twin of an Industrial Workstation: A Novel Method of an Auto-Labeled Data Generator Using Virtual Reality for Human Action Recognition in the Context of Human–Robot Collaboration. Eng. Appl. Artif. Intell.; 2023; 118, 105655. [DOI: https://dx.doi.org/10.1016/j.engappai.2022.105655]
49. De Marchi, J.A.; Baalbergen, E.H. Towards a Human-Centric Digital Twin Architecture for Industry 5.0: Aiding Skilled Operators with Composites Production Automation. J. Phys. Conf. Ser. Inst. Phys.; 2023; 2526, 012047. [DOI: https://dx.doi.org/10.1088/1742-6596/2526/1/012047]
50. Dobaj, J.; Riel, A.; Macher, G.; Egretzberger, M. Towards DevOps for Cyber-Physical Systems (CPSs): Resilient Self-Adaptive Software for Sustainable Human-Centric Smart CPS Facilitated by Digital Twins. Machines; 2023; 11, 973. [DOI: https://dx.doi.org/10.3390/machines11100973]
51. Franciosi, C.; Miranda, S.; Veneroso, C.R.; Riemma, S. Investigating Human Factors Integration into DT-Based Joint Production and Maintenance Scheduling. IFIP Advances in Information and Communication Technology; Alfnes, E.; Romsdal, A.; Strandhagen, J.O.; von Cieminski, G.; Romero, D. Springer Science and Business Media Deutschland GmbH: New York, NY, USA, 2023; Volume 689, pp. 633-648.
52. Gallego-García, S.; García-García, M. Human Needs Evolution and Organizational Development: A Case Study Analysis towards the Fifth Industrial Revolution. Adv. Sci. Technol.; 2023; 132, pp. 453-463.
53. Han, B.; Schotten, H.D. Multi-Sensory HMI for Human-Centric Industrial Digital Twins: A 6G Vision of Future Industry. Proceedings of the 2022 IEEE Symposium on Computers and Communications; Rhodes, Greece, 30 June–3 July 2022; Institute of Electrical and Electronics Engineers Inc.: New York, NY, USA, 2022; Volume 2022.
54. Jeong, Y.; Flores-García, E.; Piontek, S.; Wiktorsson, M. Implementing Transmission of Data for Digital Twins in Human-Centered Cyber-Physical Systems. Procedia CIRP; 2023; 120, pp. 992-997. [DOI: https://dx.doi.org/10.1016/j.procir.2023.09.113]
55. Kober, C.; Fette, M.; Wulfsberg, J.P. Challenges of Digital Twin Application in Manufacturing. Proceedings of the 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM); Kuala Lumpur, Malaysia, 7–10 December 2022; IEEE Computer Society: Los Alamitos, CA, USA, Volume 2022, pp. 162-168.
56. Kolesnikov, M.V.; Atmojo, U.D.; Vyatkin, V. Data-Driven Human Factors Enabled Digital Twin. Proceedings of the IECON 2023—49th Annual Conference of the IEEE Industrial Electronics Society; Singapore, 16–19 October 2023.
57. Kuts, V.; Marvel, J.A.; Aksu, M.; Pizzagalli, S.L.; Sarkans, M.; Bondarenko, Y.; Otto, T. Digital Twin as Industrial Robots Manipulation Validation Tool. Robotics; 2022; 11, 113. [DOI: https://dx.doi.org/10.3390/robotics11050113]
58. Lam, W.S.; Lam, W.H.; Lee, P.F. A Bibliometric Analysis of Digital Twin in the Supply Chain. Mathematics; 2023; 11, 3350. [DOI: https://dx.doi.org/10.3390/math11153350]
59. Lonsdale, H.; Gray, G.M.; Ahumada, L.M.; Yates, H.M.; Varughese, A.; Rehman, M.A. The Perioperative Human Digital Twin. Anesth. Analg.; 2022; 134, pp. 885-892. [DOI: https://dx.doi.org/10.1213/ANE.0000000000005916] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35299215]
60. Martínez-Gutiérrez, A.; Díez-González, J.; Perez, H.; Araújo, M. Towards Industry 5.0 through Metaverse. Rob. Comput. Integr. Manuf.; 2024; 89, 102764. [DOI: https://dx.doi.org/10.1016/j.rcim.2024.102764]
61. Modoni, G.E.; Sacco, M. A Human Digital-Twin-Based Framework Driving Human Centricity towards Industry 5.0. Sensors; 2023; 23, 6054. [DOI: https://dx.doi.org/10.3390/s23136054]
62. Naqvi, S.M.R.; Ghufran, M.; Meraghni, S.; Varnier, C.; Nicod, J.-M.; Zerhouni, N. Human Knowledge Centered Maintenance Decision Support in Digital Twin Environment. J. Manuf. Syst.; 2022; 65, pp. 528-537. [DOI: https://dx.doi.org/10.1016/j.jmsy.2022.10.003]
63. Pang, J.; Zheng, P. An MBD-Enabled Digital Twin Modeling Method for Cognition Assistance in Human-Centric Smart Assembly. Proceedings of the 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE); Auckland, New Zealand, 26–30 August 2023; Volume 2023.
64. Paul, G.; Briceno, L. A Conceptual Framework of DHM Enablers for Ergonomics 4.0. Lecture Notes Networks Systems; Black, N.L.; Neumann, W.P.; Noy, I. Springer Science and Business Media Deutschland GmbH: New York, NY, USA, 2022; Volume 223, pp. 403-406.
65. Perez, M.-J.; Meza, S.-M.; Bravo, F.-A.; Trentesaux, D.; Jimenez, J.-F. Evolution of the Human Digital Representation in Manufacturing Production Systems. Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future; Borangiu, T.; Trentesaux, D.; Leitão, P.; Cardin, O.; Joblot, L. Springer Science and Business Media Deutschland GmbH: New York, NY, USA, 2022; Volume 1034, pp. 201-211.
66. 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]
67. Erol, T.; Mendi, A.F.; Doğan, D. The Digital Twin Revolution in Healthcare. Proceedings of the 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT); Istanbul, Turkey, 22–24 October 2020; pp. 1-7.
68. Tu, X.; Autiosalo, J.; Ala-Laurinaho, R.; Yang, C.; Salminen, P.; Tammi, K. TwinXR: Method for Using Digital Twin Descriptions in Industrial eXtended Reality Applications. Front. Virtual Real.; 2023; 4, 1019080. [DOI: https://dx.doi.org/10.3389/frvir.2023.1019080]
69. Wang, H.; Lv, L.; Li, X.; Li, H.; Leng, J.; Zhang, Y.; Thomson, V.; Liu, G.; Wen, X.; Sun, C. et al. A Safety Management Approach for Industry 5.0′s Human-Centered Manufacturing Based on Digital Twin. J. Manuf. Syst.; 2023; 66, pp. 1-12. [DOI: https://dx.doi.org/10.1016/j.jmsy.2022.11.013]
70. Shengli, W. Is Human Digital Twin Possible?. Comput. Methods Programs Biomed. Update; 2021; 1, 100014. [DOI: https://dx.doi.org/10.1016/j.cmpbup.2021.100014]
71. Zhu, Q.; Huang, S.; Wang, G.; Moghaddam, S.K.; Lu, Y.; Yan, Y. Dynamic Reconfiguration Optimization of Intelligent Manufacturing System with Human-Robot Collaboration Based on Digital Twin. J. Manuf. Syst.; 2022; 65, pp. 330-338. [DOI: https://dx.doi.org/10.1016/j.jmsy.2022.09.021]
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; Fani, Virginia 2
; Bandinelli, Romeo 2
1 Department of Industrial Engineering, University of Florence, Viale Morgagni 40/44, 50134 Florence, Italy;
2 Department of Industrial Engineering, University of Florence, Viale Morgagni 40/44, 50134 Florence, Italy;