Ergonomics Risk Assessment for Manual Material Handling of Warehouse Activities Involving High Shelf and Low Shelf Binning Processes: Application of Marker-Based Motion Capture

Abstract

Lower back pain is a musculoskeletal disorder that is commonly reported among warehouse workers due to the nature of the work environment and manual handling activities. The objective of this study was to assess the ergonomic risks among warehouse workers carrying out high shelf (HS) and low shelf (LS) binning processes. A questionnaire was used to determine the prevalence of musculoskeletal symptoms, while a marker-based motion capture (MoCap) system worksheet was used to record the participants’ motion and determine the action risk level. A total of 33% of the participants reported lower back pain in the past seven days, based on the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) results. Analysis of the body velocities showed that the HS binning process had four major velocity peaks, defined as the initial, lowering, lifting, and final phases. In comparison, the LS binning process had two major peaks defined, the crouching and rising phases. There were significant differences between the mean velocities of the workers for the HS binning process, indicating that the workers have different movement patterns with varying velocities.

1. Introduction

Work Musculoskeletal Disorder (WMSD) can be defined as injuries or disorders related to muscles, nerves, tendons, joints, cartilages, and spinal discs [1]. Common WMSDs are low back pain and carpal tunnel syndrome, which often result from unergonomic working conditions [2]. Low back pain is a major healthcare problem with a high lifetime prevalence [3]. Among the causes of WMSDs are repetitive motion, excessive force, awkward or sustained posture, and prolonged sitting or standing. Manual material handling (MMH) is one of the main factors contributing to WMSDs among workers in industrial developing countries such as Malaysia [4]. Furthermore, most companies in Malaysia do not practice ergonomic guidelines since such guidelines have not been legislated [5].

The economic aspects of ergonomics are another interesting subject of ergonomics management. There are two different approaches to ergonomic programs, namely proactive and reactive approaches. The proactive approach is more economically favorable because the intervention is carried out at the design and planning stages of the production system, where the costs of making changes are the lowest. However, companies tend to adopt a reactive approach to analyzing ergonomic issues within the production system when injuries and illnesses have already occurred. These ergonomic programs aim to minimize the associated risks by understanding the negative impacts of ergonomic injuries and illnesses on business, thereby reducing the costs involved [6].

Biomechanics is the study of human kinematic variables that describe human motion in terms of position, velocity, and acceleration [7,8,9,10]. Biomechanic characteristics such as the angle of trunk flexion and extension, velocity, acceleration, spine patterns, and muscle forces are studied to evaluate lower back disorders [11,12]. Motion capture systems can be applied to capture human motion in performing biomechanical studies for ergonomic risk assessment [7,8].

Optical motion capture can be classified into marker-based and markerless, with each system possessing its own set of strengths and weaknesses depending on its use [13,14]. Markerless motion capture can operate with minimal cost and simple startup but with less accuracy, while maker-based motion capture is more accurate with relatively high cost [13,15]. Both marker-based and markerless motion capture systems are extensively used in biomechanical studies [14,16].

Human activity recognition (HAR) is another technique with the potential to be applied in ergonomic risk assessment, apart from its current application in sports, interactive gaming, and smart home systems [17]. Through the convolutional neural network (CNN), deep learning can automatically learn intricate activity features to address HAR tasks [18,19]. The method requires proper and systematic labeling through sufficient human and computing resources to increase efficiency [20].

Warehouse workers who perform manual handling tasks such as lifting, pushing, and pulling are most likely to be exposed to back injury compared to other departments that perform fewer manual handling tasks [21,22]. The various physical tasks regularly carried out by warehouse workers may result in the development of WMSDs. Therefore, ergonomic assessment is crucial in order to provide an ergonomic workplace in line with the third goal of the United Nations Sustainable Development Goals, which is good health and well-being.

Warehouse workers regularly carry out manual material handling activities in in-store departments, including reaching above shoulder height for the high shelf (HS) binning process and lowering below knee height for the low shelf (LS) binning process, which could lead to the development of WMSDs. This is due to the fact that there is a potential for awkward postures stemming from the need physically to reach high and lower the body for these two activities [23,24]. Therefore, this study aims to evaluate the ergonomic risks of warehouse workers by using a marker-based motion capture system. These ergonomic risks are assessed by analyzing the angle and velocities of specific body parts for the HS and LS binning processes.

2. Methods

A store department of a manufacturing company was selected as part of this study (including the description of the tasks of the warehouse workers). The participants were selected based on the inclusion and exclusion criteria. Only workers aged more than 18 years old with normal BMI and with at least one year of work experience were included in this study. Workers diagnosed with chronic diseases or injuries were excluded from this study.

Ten participants were willing to participate in the study. Four participants were excluded for not fulfilling the age, work experience, or chronic diseases criteria. A total of 6 participants (4 males and 2 females) were selected following the inclusion criteria. A total of 3 participants (2 males and 1 female) were excluded from the study due to high BMI. Therefore, the remaining 3 participants (2 males and 1 female) were selected.

The musculoskeletal pain/discomfort survey and Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) were used to assess each participant’s work experience relating to musculoskeletal discomfort. All participants were required to indicate their level of musculoskeletal discomfort in any of their body parts during the past seven days and 12 months. The questionnaire was collected after the completion of the study on the same day.

The task selection was based on a structured interview with company-related safety personnel regarding the ergonomically challenging tasks that frequently produced musculoskeletal discomfort among workers. From the results of this interview, the tasks selected were the high shelf (HS) and low self (LS) binning processes, which involve reaching above shoulder height and lowering below knee height, and which were identified as critical activities in the warehouse. The conditions of the experimental setup were designed based on field observations. Table 1 summarizes the experimental setup conditions

Each participant was requested to wear a motion capture suit with 25 markers attached to different parts of the participant’s body, including the back of the head, chest, spine, arms, hand and shoulder, elbows, wrist, thigh, knee, and legs. All the participants performed the HS and LS binning process according to the respective procedure.

The procedure for the HS binning process was as follows: (1) T-posture with both hands raised level to shoulder for five seconds; (2) walking to a table to retrieve an item; (3) climbing a ladder to reach a bin; (4) placing the bin on the ground and putting the item into the bin; (5) placing the bin back on the shelf.

The procedure for the LS binning process was as follows: (1) T-posture with both hands raised level to shoulder for five seconds; (2) walking to a table to retrieve an item; (3) crouching down on the floor; (4) pulling out a duty bin and putting the item into the bin; (5) placing the bin back to the initial position.

Eight high-speed Osprey cameras were used to record the motion of the participants. The cameras can record 245 frames per second (FPS) with a sensor size of 640 × 480 pixels, which are used to detect the infrared reflection from all the markers within a surrounding area of 5 m × 5 m [25,26]. For this study, 60 FPS was used as the sampling rate. The system was calibrated before the experiment. The Cortex 4.0 software processed the data obtained from the cameras. The Cortex 4.0 software converted the marker motion to a digital human model with cartesian coordinates (x, y z) [26]. The software acts as a skeleton builder to visualize the movements and produce full-body kinetics and kinematic measurements. All the data involving the trajectory time, displacement, velocity, and acceleration were extracted to Microsoft Excel for further analysis.

The body angle deviation was calculated from the skeleton diagram developed by the motion capture Cortex 4.0 software. The Pythagorean Theorem and Law of Cosines were applied in calculating the joint angles between 3 respective markers, as shown in Figure 1. The body angle deviation calculated was then compared with the body velocity data to evaluate the ergonomic risk of the selected tasks. The traditional ergonomic assessment approach only focuses on assessing the posture of movement and neglects the motion of the movement [27]. Motion capture provided the kinematic data of body movement and is believed to be an option for providing reliable data to evaluate the ergonomic risk [28,29].

The Pythagorean theorem was used to formulate a triangle from three points of markers. The distances were then computed by using the X-Y-Z coordinates, which can further be used to calculate the angular movement of certain body parts by using the Law of Cosines [30].

(1)$\overrightarrow{AB}=B-A$

(2)$\overrightarrow{BC}=C-B$

where $A=\left(x_1,y_1,z_1\right)$,$B=\left(x_2,y_2,z_2\right)$, $C=\left(x_3,y_3,z_3\right)$.

The scalar product of both vectors (dot product) gives the following equation:

(3)$\overrightarrow{AB}·\overrightarrow{BC}=\Vert \overrightarrow{AB}\Vert \Vert \overrightarrow{BC}\Vert \cos \theta$

where $\Vert \ast \Vert$ determines the length, and $\theta$ gives the angle between the two vectors of $\overrightarrow{AB}$ and $\overrightarrow{BC}$

(4)$\theta ={\cos }^{-1}\left(\frac{\overrightarrow{AB}\cdot \overrightarrow{BC}}{\Vert \overrightarrow{AB}\Vert \Vert \overrightarrow{BC}\Vert }\right)$

The body velocity data were collected from the motion capture Cortex 4.0 software. The Cortex 4.0 software provides real-time data that include displacement, velocity, and acceleration. The data then went through a cleaning up process during the post-processing phase before being extracted to Microsoft Excel.

The study on the ergonomic risk assessment using human kinematic data is a newly proposed method that is still in the research and discussion phase. The method’s reliability is compared to a Rapid entire body assessment (REBA) for the same motions. REBA is a postural analysis system that is sensitive to musculoskeletal risks in various tasks. It was developed by incorporating dynamic and static postural loading factors, a human load interface, and a new gravity-assisted upper limb position concept. REBA is a well-known ergonomic risk assessment tool that provides a systematic procedure to assess the biomechanical and postural loading of the human body [7]. Figure 2 describes the overall research process.

3. Results and Discussion

The questionnaire survey consisted of 6 male workers and 3 female workers aged 22 to 34. Their mean age, height, and BMI were 26.325 ± 3.11 years old, 162.11 ± 4.43 cm, and 26.32 ± 6.58 kg/m², respectively. A total of 66.7% of the respondents had 1–2 years of working experience, and 55.6% worked 8 h a day. Only 33.3% of the respondents have working experience of 5 years and above, out of which 22.2% work 9 h a day, and 11.1% work more than 9 h a day.

Based on the data collected from the Cornell Musculoskeletal Discomfort survey (Figure 3), all respondents reported pain in certain parts of their bodies. As shown in Figure 3, more than half of the respondents reported a one-week prevalence of lower back pain, and 44% experienced neck, left shoulder, upper arm, thigh, and lower leg pain. It shows that the various lifting tasks performed by the warehouse workers may be the primary cause of lower back pain [21].

On the other hand, 33% of the respondents reported moderately uncomfortable pain in the lower legs and feet. In addition, the reported pain in the lower back slightly interfered with the ability to work for 33% of respondents. In comparison, 22% reported pain in the neck, thighs, knees, lower legs, and feet, which slightly interfered with the ability to work. The reports of pain in various body parts are consistent with the findings, as the warehouse workers were involved in various MMH tasks [31].

Table 2 shows all participants’ REBA scores for the HS and LS binning process. From the result shown in Table 2, all participants’ grand REBA score for the task is more than 8, except for the crouching task by participants A and B for the HS binning process. Most of the score falls into action level 3, which indicates that the posture poses a high risk of injury to the worker. The awkward working posture may induce a risk for OMSDs during prolonged work. Thus, investigations are needed, and changes in reaching posture need to be implemented to lessen the store employees’ exposure to MSDs’ risks.

When the individual score was being reviewed, it was found that the relatively high upper arm score contributed to the high grand REBA score. A detailed analysis of the joint angles also found that the shoulder is often raised and abducted with an upward rotation position, and the arm is always abducted. It shows that the height of the top shelf, which is 7 ft (213.36 cm), may be relatively high compared with the warehouse employees, who have a mean height of 162.11 ± 4.43 cm. Despite having an additional height of 44 cm using a small ladder, employees still have to perform lifting or reaching above shoulder height level. Hence, a relatively large upper arm abduction will be formed, which causes the shoulder to be abducted or flexed.

A review of the individual score shows that the grand REBA score falls into level 2 due to the relatively high neck, legs, and upper arm score for the crouching motion in the HS binning process. A detailed analysis of the joint angles shows that the neck and elbow are often flexed when the participant looks down to place an item into the bin. Awkward neck posture with excessive flexion induces lower back and neck pain risks.

Individual review of the REBA scores shows that the leg and upper arm have relatively high scores, which contributed significantly to the high grand REBA score. From the detailed analysis of the joints, it was found that all participants also crouched and had a toe-off position with the right rear foot bent upwards and heel lifted during the activity, similar to the posture in the HS binning process. Thus, employees should crouch with both feet flat on the ground to avoid foot muscle tension and to provide extra stability in the posture.

For ergonomic assessment using motion capture, three participants were involved in this simulation. Figure 4a–c shows the reaching and crouching activities in the HS and LS binning process. The body angle deviation was calculated from the body skeleton diagram in this simulation. Table 3 shows the deviation of body angles during different postures in the HS and LS binning process.

Overall, all the participants completed the task around 25 s to 30 s for the HS binning process and 10 s to 15 s for the LS binning process. Based on the two tasks, the reaching and crouching postures, as shown in Figure 4, were identified to have the potential to cause injury. For HS binning process reaching postures, all three workers are required to stress their arms above their height to reach the item on the shelf. This overhead lifting posture required high shoulder flexion and neck extension, developing muscle stress and fatigue. Lifting height and lifted weight always become an issue of concern to the above-shoulder reaching posture [32]. Although the weight load of the task was lower than the recommended weight load, the lifting height and presence of a stepladder still affected the performance of the HS binning process [33]. During the crouching phase of the HS binning process, participants B and C performed full squatting while participant A performed stooping, which caused participant A to develop more trunk and neck deviation angles than other participants, as shown in Table 3. Bending the trunk eventually causes tension in the musculoskeletal system, especially in the spine region, and hence triggers MSD [34]. In short, the three participants performed the HS binning process with similar postures except during crouching.

All the participants kneeled and crouched on the ground to pull and push a bin into the lower shelf for the LS binning process. As all the participants performed the crouching with a proper squatting posture, they did not develop much angle deviation for the trunk and neck except the knee. In completing the task, the workers are required to perform high knee flexion with a full squatting posture. Briefly, all the participants performed the LS binning process with equal movement and phase.

For the crouching posture in the HS binning process, an awkward posture during the activity was caused by a high angle deviation for the neck, which was recorded at more than 25° and with a median neck flexion of 27.08°, as shown in Table 3. At the same time, the angle deviation of the neck, which was recorded for the crouching activity in the LS binning process, was lower compared to the HS binning process. The high neck flexion angle during the HS binning process occurred because all the participants tended to look at the item during the process that required their neck to bend forward. The neck posture significantly affects the neck muscle, where the flexion causes greater muscle activity than neutral and extension neck postures. Hence, awkward postures and the percentage of time spent with the neck and trunk flexing more than 20° can cause musculoskeletal discomfort in the lower back and neck regions [35].

For the lower and upper arm, both the HS and LS binning process for all postures showed high angle deviation outside the recommended angle. The recommended angles for the upper and lower arm are 0–60° and 90°–120°, respectively [36]. The high deviation angle of the lower and upper arm for both the HS and LS binning process occurred because all the participants put the bin far from their body, which required the participant to stress their arms to reach the bin. Awkward postures at the shoulder area can decrease the biomechanical advantage during manual handling and expose the worker to musculoskeletal injury [37].

Two of the participants, denoted as participants B and C, recorded high knee angles in the HS binning process, with angles recorded at 130.1° and 120.12°, respectively. In contrast, participant A recorded only an angle of 27.36° in the crouching posture. Participant A, who placed the bin on the ladder instead of on the ground, recorded lower knee angles as more flexion was required to kneel. The angle deviation of crouching in the LS binning process also recorded a high deviation, with a median flexion of 124.5°. High knee flexion postures that exceed the recommended posture are defined as possessing angles of more than 120° flexion [38,39]. Workers who often assume high knee flexion postures also increase the risk of degenerative knee diseases such as knee osteoarthritis [40,41].

The mean angles of the wrist for crouching and reaching in the HS binning process exceeded the recommended angle of 15° [42]. The wrist position should be kept neutral to prevent the compression stress at the carpal tunnel due to awkward wrist posture. This is due to the fact that the strain and stress accumulation on the wrist can lead to MSDs [42].

Low angle deviation at the trunk region was recorded by the participants for both the HS and LS binning process, with a median deviation of angle 4.87° for the HS crouching posture, 12.77° for the HS reaching posture, and 4.90° for the LS crouching posture.

This study focused on the motion of the head and spine region for the motion capture analysis. It is because the lower back is the most reported body part for pain experienced by workers, as shown in Figure 3. Analyzing the movement of the head, upper spine, middle spine, and lower spine in terms of body velocity can determine whether the selected movement directly affects the reported pain [12,27]. The trunk muscle coordination during the flexion and extension movement significantly affects spinal health [12]. Figure 5 and Figure 6 show the velocity profile of the HS and LS binning process for the neck and spine, respectively.

The increasing body velocity associated with body acceleration can contribute to the high body angle deviation [27]. High body angle deviation contributes to an awkward posture, which has a high risk of ergonomic movement. An awkward posture is the deviation of the body from its natural position that has the potential to cause musculoskeletal injury [43]. The data of body velocity obtained from the motion-capture of the workers’ activities can indicate the awkward posture involved during working.

The overall trend for the head and spine velocity from the HS binning process consists of four major peaks, which are defined as the initial phase for the first peak, lowering for the second peak, lifting for the third peak, and the final phase for the final peak. For the head and spine velocity from the LS binning process, the overall trend showed two major peaks defined as the initial phase (lowering) for the first peak and the final phase (rising) for the second peak.

Participant A recorded peak velocities for the middle and lower spine during the beginning phases, as shown in Figure 5a. This was due to participant A climbing up the step ladder at a faster pace compared to the other participants. Referring to Figure 6a, participant A recorded the highest velocity for the head region out of all the participants in the final phase. Higher head velocities for participant A showed that the neck was flexed at a higher speed than the other participants in both the initial and final phases. From this scenario, we can see that participant A is executing the activities faster compared to the other participants. Working at a faster pace can increase muscle recruitment during the initial phase but results in a decrease in muscle recruitment in the terminal phase. It is important to work at a suitable pace to control the mechanical load on the muscle and spine [44]. This trend is shown in Figure 3, as a significant number of participants reported various types of pain within the neck, shoulder, arm, and back regions during the last workweek.

From Figure 5 and Figure 6, it can be seen that the velocity increases and then decreases dramatically. This pattern shows that the movement is very vigorous for the respective markers, which indicates that many irregular movements are involved. Irregular movement means that there are sudden changes in the movement, resulting in large velocity changes. An example of irregular movement, along with the associated increase in velocity, can be seen clearly at 12 s in Figure 6, which shows the LS binning process. All participants recorded sudden high velocity when they stood up from crouching. The high levels of an inertial force of the lifted box can cause jerky contraction of the muscles and consequently increase the risk of lumbar spine injury [27,45]. Back compression forces peak toward the beginning of a lift [46].

Body velocity and inertial forces are two parameters that are closely intertwined. High inertial forces are the result of high acceleration occurring at the beginning of the various movements carried out by the participants [27]. In turn, high body acceleration leads to a rapid increase in body velocity. Irregular movement always happens during the initial phase for most of the movements. As shown in Figure 5, all the participants recorded a rapid increase in velocities before they successfully crouched at 17 s, 22 s, and 25 s for participants A, B, and C, respectively. However, as the movements progressed, the markers for the spine and neck recorded low velocities as no movement was involved during crouching. Hence, it is beneficial to start every movement slowly to prevent the risk of injury by avoiding irregular movements during the lifting and lowering processes [45].

The velocity of the LS binning process for the neck and spine, as shown in Figure 6, also recorded a rapid increase in velocity just before the participant successfully crouched. Irregular movement occurs at around 6 s for all the participants. The irregular movement and the body angle deviation can be seen. From the data of the deviation angle for the HS and LS binning process, as shown in Table 3, most of the participants exceeded the recommended angle for the neck and trunk during the crouching posture. This is consistent with the velocity data, which show that irregular movement will result in high angle deviation that can cause awkward posture [27]. Thus, this shows that data on velocity and angle deviation closely relate to the pain reported by the worker.

The neck and trunk posture can also contribute to back discomfort and injury. The velocity and angle deviation data show that awkward postures are generated for the back area during the HS and LS binning process. Besides, most of the participants recorded increasing velocity not only during crouching. An increase in velocity can be detected in each phase of the movement for the HS binning process. It involves movement such as stepping up the ladder (initial phase), stepping down the ladder (lowering and final phases), and lifting the object during the lifting phase. All the participants recorded increasing velocity twice for the LS binning process, namely before and after crouching. Thus, the HS binning process could be a potential under-recognized risk factor for WMSDs, as it is a task that entails complex biomechanical activity due to the higher maximum velocities recorded at different phases of the process rather than the walking phase [46,47].

Therefore, the motion capture analysis focusing on the head and spine region showed that the movement of the LS and Hs binning process has a high potential to produce muscle stress in the neck, back region, and shoulder of the subject. A similar result was shown with the data obtained from questionnaires, which consist of body pain experienced by the worker: lower back, neck, and shoulder are the most reported body pain. The result of motion capture shows irregular and jerky movement based on the velocity data of the spine and neck, potentially contributing to the lower back pain reported by most workers. This is because lower back pain is commonly reported due to sudden movement of the lumbar spine during the initiation of movement [12].

This study focuses on the body kinematic variables (velocity) in interpreting the potential risks of WMSDs. The velocity data obtained from all the participants were used to analyze the participants’ movements for the HS and LS binning process, focusing on the head and spine. The HS binning process produced more velocity peaks than the LS binning process due to higher shelf height and the requirement of a higher number of motions. However, that does not indicate that fewer motions are less risky. A study showed that ankle height low-lowering MMH work increased muscle activities on the neck and shoulder and produced more loading than above shoulder height high-lifting MMH work, which could cause neck and back pain [48]. A further increase in the height of the shelf could lead to an increase in load on the upper trapezius, supraspinatus, and infraspinatus, which could induce muscle fatigue and increase the risk of shoulder injuries [44].

High-velocity data obtained can be used to explain the pace of a motion. It is indicated that there was a biomechanical advantage of fast lifting. It is also suggested that workers be educated on the lifting pace to control unnecessary muscle recruitment and reap the benefits of fast lifting [46]. Workers should be informed and trained to carry out lifting using optimal lifting speed that gives maximum lifting capacity [49].

In the objective to apply kinematic variables for ergonomic risk assessments, the results obtained from the peak values of the velocity for the same motion reflect the results from the REBA and CMDQ. For the HS binning process, the main body parts that are affected are the back, neck, and shoulders, which are also in line with the findings in the CMDQ. Meanwhile, for the LS binning process, the back is also registered as a high value in REBA.

Variation in the motion kinematics is dependent on factors such as repetitiveness of the activity [50], the natural shape of the spine [51], the gender of the worker [52], or the mass magnitude of an object [53]. Bending is another factor that may increase the variability of body kinematics and jerky motions, which could lead to a higher risk of lower back pain [54].

The risk of WMSDs can be reduced through improvements relating to the motions of the HS and LS binning activities. Such improvements include (i) workplace design (providing a step ladder, a more systematic arrangement that is suitable for the workers, and team lifting); (ii) stretching before performing daily activities; and (iii) training and education (proper manual handling techniques and information on WMSDs).

4. Conclusions

MMH, such as HS and LS binning activities, exposes workers to the risk of WMSDs. Traditional ergonomic risk assessments can be subjective, depending on the experience and knowledge level of the assessors. In the study, an ergonomic risk assessment was conducted by applying a marker-based motion capture system to ensure the accuracy of the body angle deviation. Through the method, a kinematic variable of velocity can be assessed to detect motions that may increase the risk of WMSDs.

The musculoskeletal pain survey identified that 56% of the participants had shoulder and lower back pain within the past 12 months. The CMDQ discovered that 56% of the respondents reported that they experienced lower back pain within the past 7 days, with 33% stating that the pain was moderate and only slightly interfered with work. This survey showed that pain in the lower back region of the body is the most reported pain among warehouse workers. In addition, the assessment of the HS and LS binning process using motion capture showed a similar result to the survey. The data of angle deviation and velocity from the HS and LS binning process can show development of discomfort in the back area.

In general, data on the body angle deviation and velocity can be applied to assess the potential risk of WMSDs in workers carrying out MMH tasks. Body angle is widely used in traditional ergonomics risk assessment. Velocity can be another variable useful in identifying the exact point of motion that may expose the workers to WMSDs.

For future studies, it is important to explore the items of: (i) relationship between the body angle deviations and velocities; and (ii) acceleration as the kinematic variable to indicate the risks of WMSDs.

The overall results of this study showed that ergonomic interventions should be implemented to improve the safety and health of warehouse workers involved in MMH tasks and minimize the risk of MSDs. Future studies can focus on a more in-depth analysis of the body velocity and its relationship with body angle deviation. The analysis using the kinetic and kinematic variables can be conducted more systematically by phases and segregation of movements to ensure the efficiency of the analysis.

Author Contributions

M.H.J., A.S.A.M. and N.Z.A. led the conceptualization, supervision, and funding acquisition of the study. Y.S.Z., N.A. and M.H.J. conducted the investigation, methodology, writing—original draft preparation, and formal analysis. The writing components—review and editing and project administration were conducted by N.A. and M.H.J. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was granted approval for implementation by the Jawatankuasa Etika Penyelidikan Manusia Universiti Sains Malaysia (Code No.: USM/JEPeM/19080456).

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Joint Angles between 3 Respective Markers for the Neck, Trunk, Leg, Upper Arm, Lower Arm, and Wrist.

Figure 2. Overall research process flowchart.

Figure 3. Percentage of Respondents Experiencing Pain in Any Region of the Body During the Last Work Week, including the Intensity of Pain and Interference of Pain with the Ability to Work.

Figure 4. Skeleton Diagram of the Participant Carrying Out the HS Binning Process with (a) Reaching (b), Crouching Posture, and (c) Participant Kneeling and Crouching during the LS Binning Process.

Figure 5. Velocity profiles of head and spine during the HS binning process for (a) participant A, (b) participant B, and (c) participant C.

Figure 6. Velocity profiles of the head and spine during LS binning process for (a) participant A, (b) participant B, and (c) participant C.

Table 1

Variables and Conditions of the Experiment.

		Variables	Levels or Conditions
Independent variable	HS binning process	Weight of bin	18 kg
		Reaching height	210 cm–235 cm
	LS binning process	Weight of bin	2.1 kg
		Lowering height	40 cm–50 cm
Dependent variable	-	Angle of deviation for specific body parts (θ)	-
	-	Movement time (s)	-
	-	Movement velocities (mm/s)	-
Constant variable	-	Equipment	Medium duty bin
			(61.5 cm × 42 cm × 23.5 cm)
			Light duty bin
			(50 cm × 31 cm × 17 cm)
			Step ladder
			(49 cm × 50 cm × 44 cm)

Table 2

Grand REBA score of all participants for the reaching posture in the HS and LS binning process.

			REBA Score
Task		Participant	A		B		C		Max. Score
	Body Part		Reaching	Crouching	Reaching	Crouching	Reaching	Crouching
HS Binning Process	Neck		2	2	3	2	3	2	3
	Trunk		1	2	2	3	2	2	5
	Leg		4	1	1	3	1	4	4
	Upper arm		4	2	5	3	5	2	6
	Lower arm		2	2	2	2	2	2	2
	Wrist		2	2	2	2	3	1	3
	Load		0	0	0	0	0	0	4
	Neck, trunk, leg posture		5	3	4	6	4	6	9
	Arm and wrist posture		8	3	8	5	8	2	9
	Entire Body posture		8	3	8	8	8	6	12
	Activity Score		1	1	1	1	1	1	3
	Grand Score		8	4	9	9	9	7	15
LS Binning Process	Neck		-	2	-	2	-	2	3
	Trunk		-	1	-	1	-	2	5
	Leg		-	4	-	4	-	4	4
	Upper arm		-	4	-	3	-	4	6
	Lower arm		-	2	-	1	-	1	2
	Wrist		-	2	-	2	-	3	3
	Load		-	2	-	2	-	2	4
	Neck, trunk, leg posture		-	7	-	7	-	8	9
	Arm and wrist posture		-	6	-	4	-	5	9
	Entire Body posture		-	9	-	8	-	10	12
	Activity Score		-	1	-	1	-	1	3
	Grand Score		-	10	-	9	-	11	15

Table 3

Deviation of Body Angles with its mean (±SD) for the Crouching and Reaching Postures of all Participants in the HS and LS Binning Process.

				Deviation of Angle (°)
Task	Posture		Participant	A	B	C	Median
		Body Part
HS Binning Process	Crouching	Neck		38.24	24.63	27.08	27.08
		Trunk		11.47	4.87	2.39	4.87
		Knee		27.36	130.1	120.12	120.12
		Upper arm		32.78	54.29	22.45	32.78
		Lower arm		44.32	40.21	8.44	40.21
		Wrist		21.4	23.92	6.69	21.4
	Reaching	Neck		8.87	1.67	8.03	8.03
		Trunk		14.38	10.92	12.77	12.77
		Knee		67.2	8.72	9.07	9.07
		Upper arm		89.55	92.84	90.9	90.90
		Lower arm		40.15	23.93	49.15	40.15
		Wrist		7.73	0.58	24.75	7.73
LS Binning Process	Crouching	Neck		18.60	9.60	22.99	18.60
		Trunk		6.05	4.90	1.91	4.90
		Knee		117.09	124.50	127.03	124.5
		Upper arm		65.22	37.74	51.24	51.24
		Lower arm		11.79	65.20	66.04	65.20
		Wrist		3.67	0.69	44.05	3.67

References

1. CDC. Work-Related Musculoskeletal Disorders & Ergonomics. Centers for Disease Control and Prevention. 2020; Available online: https://www.cdc.gov/workplacehealthpromotion/health-strategies/musculoskeletal-disorders/index.html (accessed on 14 February 2022).

2. Ge, H.; Sun, X.; Liu, J.; Zhang, C. The status of musculoskeletal disorders and its influence on the working ability of Oilworkers in Xinjiang, China. Int. J. Environ. Res. Public Health; 2018; 15, 842. [DOI: https://dx.doi.org/10.3390/ijerph15050842] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29695120]

3. Al-Hadidi, F.; Bsisu, I.; Haddad, B.; AlRyalat, S.A.; Shaban, M.; Matani, N.; Dehidi, S.; Khater, Y.; Shahrouri, R.; Muzayen, T. et al. The prevalence of low back pain among female hospital staff at childbearing age. PeerJ; 2020; 8, e9199. [DOI: https://dx.doi.org/10.7717/peerj.9199] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32607279]

4. Parida, R.; Ray, P.K. Biomechanical Modelling of Manual Material Handling Tasks: A Comprehensive Review. Procedia Manuf.; 2015; 3, pp. 4598-4605. [DOI: https://dx.doi.org/10.1016/j.promfg.2015.07.539]

5. Hazana Abdullah, N.; Aziati Abdul Hamid, N.; Wahab, E.; Shamsuddin, A.; Asmawi, R. Work-related Musculoskeletal Disorder (WRMD) among Production Operators: Studies of Differences in Age and Gender. J. Phys. Conf. Ser.; 2018; 1049, 012023. [DOI: https://dx.doi.org/10.1088/1742-6596/1049/1/012023]

6. Fernandes, P.R.; Hurtado, A.L.B.; Batiz, E.C. Ergonomics management with a proactive focus. Procedia Manuf.; 2015; 3, pp. 4509-4516. [DOI: https://dx.doi.org/10.1016/j.promfg.2015.07.465]

7. Petrosyan, T.; Dunoyan, A.; Mkrtchyan, H. Application of Motion Capture Systems in Ergonomic Analysis. Armen. J. Spec. Educ.; 2020; 1, pp. 107-117. [DOI: https://dx.doi.org/10.24234/se.2020.1.1.157]

8. Ong, Z.C.; Seet, Y.C.; Khoo, S.Y.; Noroozi, S. Development of an economic wireless human motion analysis device for quantitative assessment of human body joint. Meas. J. Int. Meas. Conf.; 2018; 115, pp. 306-315. [DOI: https://dx.doi.org/10.1016/j.measurement.2017.10.056]

9. Ro, D.H.; Han, H.S.; Lee, D.Y.; Kim, S.H.; Kwak, Y.H.; Lee, M.C. Slow gait speed after bilateral total knee arthroplasty is associated with suboptimal improvement of knee biomechanics. Knee Surg. Sport Traumatol. Arthrosc.; 2018; 26, pp. 1671-1680. [DOI: https://dx.doi.org/10.1007/s00167-017-4682-8]

10. Halilaj, E.; Rajagopal, A.; Fiterau, M.; Hicks, J.L.; Hastie, T.J.; Delp, S.L. Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities. J. Biomech.; 2018; 81, pp. 1-11. [DOI: https://dx.doi.org/10.1016/j.jbiomech.2018.09.009]

11. Li, X.; Komeili, A.; Gül, M.; El-Rich, M. A framework for evaluating muscle activity during repetitive manual material handling in construction manufacturing. Autom. Constr.; 2017; 79, pp. 39-48. [DOI: https://dx.doi.org/10.1016/j.autcon.2017.01.005]

12. D’hooge, R.; Hodges, P.; Tsao, H.; Hall, L.; MacDonald, D.; Danneels, L. Altered trunk muscle coordination during rapid trunk flexion in people in remission of recurrent low back pain. J. Electromyogr. Kinesiol.; 2013; 23, pp. 173-181. [DOI: https://dx.doi.org/10.1016/j.jelekin.2012.09.003]

13. Patrizi, A.; Pennestrì, E.; Valentini, P.P. Comparison between low-cost marker-less and high-end marker-based motion capture systems for the computer-aided assessment of working ergonomics. Ergonomics; 2016; 59, pp. 155-162. [DOI: https://dx.doi.org/10.1080/00140139.2015.1057238]

14. Perrott, M.A.; Pizzari, T.; Cook, J.; McClelland, J.A. Comparison of lower limb and trunk kinematics between markerless and marker-based motion capture systems. Gait Posture; 2017; 52, pp. 57-61. [DOI: https://dx.doi.org/10.1016/j.gaitpost.2016.10.020]

15. Lau, B.Y.S.; Ting, H.Y.; Tan, Y.W.D. Cost-Benefit Analysis Reference Framework for Human Motion Capture and Analysis Systems. Adv. Sci. Lett.; 2018; 24, pp. 1249-1253. [DOI: https://dx.doi.org/10.1166/asl.2018.10726]

16. Schmitz, A.; Ye, M.; Boggess, G.; Shapiro, R.; Yang, R.; Noehren, B. The measurement of in vivo joint angles during a squat using a single camera markerless motion capture system as compared to a marker based system. Gait Posture; 2015; 41, pp. 694-698. [DOI: https://dx.doi.org/10.1016/j.gaitpost.2015.01.028]

17. Gao, W.; Zhang, L.; Teng, Q.; He, J.; Wu, H. DanHAR: Dual attention network for multimodal human activity recognition using wearable sensors. Appl. Soft Comput.; 2021; 111, 107728. [DOI: https://dx.doi.org/10.1016/j.asoc.2021.107728]

18. Huang, W.; Zhang, L.; Gao, W.; Min, F.; He, J. Shallow convolutional neural networks for human activity recognition using wearable sensors. IEEE Trans. Instrum. Meas.; 2021; 70, 2510811. [DOI: https://dx.doi.org/10.1109/TIM.2021.3091990]

19. Gao, W.; Zhang, L.; Huang, W.; Min, F.; He, J.; Song, A. Deep neural networks for sensor-based human activity recognition using selective kernel convolution. IEEE Trans. Instrum. Meas.; 2021; 70, 2512313. [DOI: https://dx.doi.org/10.1109/TIM.2021.3102735]

20. He, J.; Zhang, Q.; Wang, L.; Pei, L. Weakly supervised human activity recognition from wearable sensors by recurrent attention learning. IEEE Sens. J.; 2018; 19, pp. 2287-2297. [DOI: https://dx.doi.org/10.1109/JSEN.2018.2885796]

21. Basahel, A.M. Investigation of Work-related Musculoskeletal Disorders (MSDs) in Warehouse Workers in Saudi Arabia. Procedia Manuf.; 2015; 3, pp. 4643-4649. [DOI: https://dx.doi.org/10.1016/j.promfg.2015.07.551]

22. Rahman, M.N.A.; Zuhaidi, M.F.A. Musculoskeletal symptoms and ergonomic hazards among material handlers in grocery retail industries. IOP Conf. Ser. Mater. Sci. Eng.; 2017; 226, 012027. [DOI: https://dx.doi.org/10.1088/1757-899X/226/1/012027]

23. Ray, P.K.; Parida, R.; Saha, E. Status Survey of Occupational Risk Factors of Manual Material Handling Tasks at a Construction Site in India. Procedia Manuf.; 2015; 3, pp. 6579-6586. [DOI: https://dx.doi.org/10.1016/j.promfg.2015.07.279]

24. Dabholkar, T.; Yardi, S.; Dabholkar, Y.G.; Velankar, H.K.; Ghuge, G. A survey of work-related musculoskeletal disorders among otolaryngologists. Indian J. Otolaryngol. Head Neck Surg.; 2017; 69, pp. 230-238. [DOI: https://dx.doi.org/10.1007/s12070-017-1106-5]

25. Syu, Y.-S.; Chen, L.; Tu, Y.-F. A Case Study of Digital Preservation of Motion Capture for Bā Jiā Jiāng Performance, Taiwan Religious Performing Arts. Euro-Mediterr. Conf.; 2018; 11197, pp. 103-110.

26. Soh, A.A.S.A.; Jafri, M.Z.M.; Azraai, N.Z. Power estimation of martial arts movement with different physical, mood, and behavior using motion capture camera. Optics for Arts, Architecture, and Archaeology VI; International Society for Optics and Photonics: San Diego, CA, USA, 2017; Volume 10331, 103310X.

27. Yunus, M.N.H.; Jaafar, M.H.; Mohamed, A.S.A.; Azraai, N.Z.; Hossain, M.S. Implementation of kinetic and kinematic variables in ergonomic risk assessment using motion capture simulation: A review. Int. J. Environ. Res. Public Health; 2021; 18, 8342. [DOI: https://dx.doi.org/10.3390/ijerph18168342] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34444087]

28. Antwi-Afari, M.F.; Li, H.; Edwards, D.J.; Pärn, E.A.; Seo, J.; Wong, A.Y.L. Biomechanical analysis of risk factors for work-related musculoskeletal disorders during repetitive lifting task in construction workers. Autom. Constr.; 2017; 83, pp. 41-47. [DOI: https://dx.doi.org/10.1016/j.autcon.2017.07.007]

29. Akhavian, R.; Behzadan, A.H. Smartphone-based construction workers’ activity recognition and classification. Autom. Constr.; 2016; 71, pp. 198-209. [DOI: https://dx.doi.org/10.1016/j.autcon.2016.08.015]

30. Chung, K.L.; Lai, J.Y. An efficient law-of-cosine-based search for vector quantization. Pattern Recognit. Lett.; 2004; 25, pp. 1613-1617. [DOI: https://dx.doi.org/10.1016/j.patrec.2004.06.007]

31. Hoozemans, M.J.M.; Knelange, E.B.; Frings-Dresen, M.H.W.; Veeger, H.E.J.; Kuijer, P.P.F.M. Are pushing and pulling work-related risk factors for upper extremity symptoms? A systematic review of observational studies. Occup. Environ. Med.; 2014; 71, pp. 788-795. [DOI: https://dx.doi.org/10.1136/oemed-2013-101837]

32. Blache, Y.; Desmoulins, L.; Allard, P.; Plamondon, A.; Begon, M. Effects of height and load weight on shoulder muscle work during overhead lifting task. Ergonomics; 2015; 58, pp. 748-761. [DOI: https://dx.doi.org/10.1080/00140139.2014.980336]

33. Manap, M.; Amat, H.; Sharif, S.; Yusoff, N.; Rahman, N. Guideline for Manual Handling at Workplace; Department of Occupational Safety and Health: Kuala Lumpur, Malaysia, 2017.

34. Deros, B.M.; Daruis, D.D.I.; Basir, I.M. A study on ergonomic awareness among workers performing manual material handling activities. Procedia Soc. Behav. Sci.; 2015; 195, pp. 1666-1673. [DOI: https://dx.doi.org/10.1016/j.sbspro.2015.06.238]

35. Palmer, K.T.; Smedley, J. Work relatedness of chronic neck pain with physical findings—A systematic review. Scand. J. Work Environ. Health; 2007; 33, pp. 165-191. [DOI: https://dx.doi.org/10.5271/sjweh.1134]

36. Aitchison, L.P.; Cui, C.K.; Arnold, A.; Nesbitt-Hawes, E.; Abbott, J. The ergonomics of laparoscopic surgery: A quantitative study of the time and motion of laparoscopic surgeons in live surgical environments. Surg. Endosc.; 2016; 30, pp. 5068-5076. [DOI: https://dx.doi.org/10.1007/s00464-016-4855-4]

37. Oliveira, A.B.; Silva, L.C.C.B.; Coury, H.J.C.G. How do low/high height and weight variation affect upper limb movements during manual material handling of industrial boxes?. Rev. Bras. Fisioter. Rev. Bras. Fisioter.; 2011; 15, pp. 494-502. [DOI: https://dx.doi.org/10.1590/S1413-35552011005000017]

38. Kingston, D.C.; Tennant, L.M.; Chong, H.C.; Acker, S.M. Peak activation of lower limb musculature during high flexion kneeling and transitional movements. Ergonomics; 2016; 59, pp. 1215-1223. [DOI: https://dx.doi.org/10.1080/00140139.2015.1130861]

39. Zelle, J.; Barink, M.; De Waal Malefijt, M.; Verdonschot, N. Thigh-calf contact: Does it affect the loading of the knee in the high-flexion range?. J. Biomech.; 2009; 42, pp. 587-593. [DOI: https://dx.doi.org/10.1016/j.jbiomech.2008.12.015]

40. Mikkelsen, S.; Pedersen, E.B.; Brauer, C.; Møller, K.L.; Alkjær, T.; Koblauch, H.; Simonsen, E.B.; Thygesen, L.C. Knee osteoarthritis among airport baggage handlers: A prospective cohort study. Am. J. Ind. Med.; 2019; 62, pp. 951-960. [DOI: https://dx.doi.org/10.1002/ajim.23044]

41. Baker, P.; Reading, I.; Cooper, C.; Coggon, D. Knee disorders in the general population and their relation to occupation. Occup. Environ. Med.; 2003; 60, pp. 794-797. [DOI: https://dx.doi.org/10.1136/oem.60.10.794]

42. Loh, P.Y.; Nakashima, H.; Muraki, S. Median nerve behavior at different wrist positions among older males. PeerJ; 2015; 3, e928. [DOI: https://dx.doi.org/10.7717/peerj.928]

43. Ezugwu, U.A.; Egba, E.N.; Igweagu, P.C.; Eneje, L.E.; Orji, S.; Ugwu, U.C. Awareness of Awkward Posture and Repetitive Motion as Ergonomic Factors Associated with Musculoskeletal Disorders by Health Promotion Professionals. Glob. J. Health Sci.; 2020; 12, 128. [DOI: https://dx.doi.org/10.5539/gjhs.v12n6p128]

44. Yoon, J.; Shiekhzadeh, A.; Nordin, M. The effect of load weight vs. pace on muscle recruitment during lifting. Appl. Ergon.; 2012; 43, pp. 1044-1050. [DOI: https://dx.doi.org/10.1016/j.apergo.2012.03.004]

45. Greenland, K.O.; Merryweather, A.S.; Bloswick, D.S. The effect of lifting speed on cumulative and peak biomechanical loading for symmetric lifting tasks. Saf. Health Work; 2013; 4, pp. 105-110. [DOI: https://dx.doi.org/10.1016/j.shaw.2013.04.001]

46. Vahdat, I.; Rostami, M.; Tabatabai Ghomsheh, F.; Khorramymehr, S.; Tanbakoosaz, A. Effects of external loading on lumbar extension moment during squat lifting. Int. J. Occup. Med. Environ. Health; 2017; 30, 665. [DOI: https://dx.doi.org/10.13075/ijomeh.1896.00896]

47. Marras, W.S.; Knapik, G.G.; Ferguson, S. Loading along the lumbar spine as influence by speed, control, load magnitude, and handle height during pushing. Clin. Biomech.; 2009; 24, pp. 155-163. [DOI: https://dx.doi.org/10.1016/j.clinbiomech.2008.10.007]

48. Hlavenka, T.M.; Christner, V.F.K.; Gregory, D.E. Neck posture during lifting and its effect on trunk muscle activation and lumbar spine posture. Appl. Ergon.; 2017; 62, pp. 28-33. [DOI: https://dx.doi.org/10.1016/j.apergo.2017.02.006]

49. Lin, C.J.; Cheng, C.-F. Lifting speed preferences and their effects on the maximal lifting capacity. Ind. Health; 2017; 55, pp. 27-34. [DOI: https://dx.doi.org/10.2486/indhealth.2016-0032]

50. Tetteh, E.; Mirka, G. Effect of Time on the Variability of Lifting Kinematics in a Repetitive Lifting Task. Proc. Hum. Factors Ergon. Soc. Annu. Meet.; 2018; 62, pp. 910-914. [DOI: https://dx.doi.org/10.1177/1541931218621209]

51. Pavlova, A.V.; Meakin, J.R.; Cooper, K.; Barr, R.J.; Aspden, R.M. Variation in lifting kinematics related to individual intrinsic lumbar curvature: An investigation in healthy adults. BMJ Open Sport Exerc. Med.; 2018; 4, e000374. [DOI: https://dx.doi.org/10.1136/bmjsem-2018-000374]

52. Pan, F.; Firouzabadi, A.; Zander, T.; Schmidt, H. Sex-dependent differences in lumbo-pelvic coordination for different lifting tasks: A study on asymptomatic adults. J. Biomech.; 2020; 102, 109505. [DOI: https://dx.doi.org/10.1016/j.jbiomech.2019.109505]

53. Elsayed, W.; Farrag, A.; El-Sayyad, M.; Marras, W. Changes in muscular activity and lumbosacral kinematics in response to handling objects of unknown mass magnitude. Hum. Mov. Sci.; 2015; 40, pp. 315-325. [DOI: https://dx.doi.org/10.1016/j.humov.2015.01.008]

54. Tsang, S.M.H.; Szeto, G.P.Y.; Li, L.M.K.; Wong, D.; Yip, M.M.P.; Lee, R.Y.W. The effects of bending speed on the lumbo-pelvic kinematics and movement pattern during forward bending in people with and without low back pain. BMC Musculoskelet. Disord.; 2017; 18, 157. [DOI: https://dx.doi.org/10.1186/s12891-017-1515-3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28415980]