Implementation of Kinetic and Kinematic Variables in Ergonomic Risk Assessment Using Motion Capture Simulation: A Review

An ergonomic evaluation using a deep learning approach for assessing postural risks in a virtual reality-based smart manufacturing context

This study proposes an integrated ergonomic evaluation designed to identify unsafe postures, whereby postural risks during industrial work are assessed in the context of virtual reality-based smart manufacturing. Unsafe postures were recognised by identifying the displacements of the centre of mass (COM) of body keypoints using a computer vision-based deep learning (DL) convolutional neural network approach. The risk levels for the identified unsafe postures were calculated using ergonomic risk assessment tools rapid upper limb assessment and rapid whole-body assessment. An analysis of variance was conducted to determine significant differences between the vertical and horizontal directions of postural movements associated with the most unsafe postures. The findings assess the ergonomic risk levels and identify the most unsafe postures during industrial work in smart manufacturing using DL method. The identified postural risks can help industry managers and researchers acquire a better understanding of unsafe postures.

Posture Evaluation of Firefighters During Simulated Fire Suppression Tasks

BACKGROUND

Posture mechanics during fire suppression tasks are associated with musculoskeletal injuries in firefighters.

METHODS

This study uses the Ovako Working Posture Analyzing System (OWAS) ergonomics tool to describe and evaluate the postures of 48 firefighters during 3 simulated tasks: (a) hose drag, (b) hose pull, and (c) high-rise pack lift. Ergonomics intervention prioritizations based on the OWAS action classification (AC) scores were identified using Wilcoxon signed-rank tests. Chi-square analyses identified associations between firefighter characteristics and OWAS AC scores.

FINDINGS

The initial hose pick-up phase of each task was identified as a high priority for ergonomics intervention (OWAS AC = 4) in 45.8%, 54.2%, and 45.8% of cases for Tasks 1, 2, and 3, respectively. Lower BMI was associated with higher AC scores for the initial hose pick-up during Task 3 (likelihood ratio = 9.20, p value = .01).

CONCLUSION

The results inform ergonomics priorities for firefighter training based on the tasks analyzed. Application to Practice: This study evaluates the posture mechanics of three commonly performed firefighting tasks. The results help inform an ergonomics training intervention focused on posture mechanics during occupational activities for firefighters.

Azure Kinect performance evaluation for human motion and upper limb biomechanical analysis

Human motion tracking is a valuable task for many medical applications. Marker-based optoelectronic systems are considered the gold standard in human motion tracking. However, their use is not always feasible in clinics and industrial environments. On the other hand, marker-less sensors became valuable tools, as they are inexpensive, noninvasive and easy to use. However, their accuracy can depend on many factors including sensor positioning, light conditions and body occlusions. In this study, following previous works on the feasibility of marker-less systems for human motion monitoring, we investigate the performance of the Microsoft Azure Kinect sensor in computing kinematic and dynamic measurements of static postures and dynamic movements. According to our knowledge, it is the first time that this sensor is compared with a Vicon marker-based system to assess the best camera positioning while observing the upper body part movements of people performing several tasks. Twenty-five healthy volunteers were monitored to evaluate the effects of the several testing conditions, including the Azure Kinect positions, the light conditions, and lower limbs occlusions, on the tracking accuracy of kinematic, dynamic, and motor control parameters. From the statistical analysis of the performed measurements, the camera in the frontal position was the most reliable, the lighting conditions had almost no effects on the tracking accuracy, while the lower limbs occlusion worsened the accuracy of the upper limbs. The assessment of human static postures and dynamic movements based on experimental data proves the feasibility of applying the Azure Kinect to the biomechanical monitoring of human motion in several fields.

Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review

Inertial measurement units (IMUs) have become the mainstay in human motion evaluation outside of the laboratory; however, quantification of 3-dimensional upper limb motion using IMUs remains challenging. The objective of this systematic review is twofold. Firstly, to evaluate computational methods used to convert IMU data to joint angles in the upper limb, including for the scapulothoracic, humerothoracic, glenohumeral, and elbow joints; and secondly, to quantify the accuracy of these approaches when compared to optoelectronic motion analysis. Fifty-two studies were included. Maximum joint motion measurement accuracy from IMUs was achieved using Euler angle decomposition and Kalman-based filters. This resulted in differences between IMU and optoelectronic motion analysis of 4° across all degrees of freedom of humerothoracic movement. Higher accuracy has been achieved at the elbow joint with functional joint axis calibration tasks and the use of kinematic constraints on gyroscope data, resulting in RMS errors between IMU and optoelectronic motion for flexion-extension as low as 2°. For the glenohumeral joint, 3D joint motion has been described with RMS errors of 6° and higher. In contrast, scapulothoracic joint motion tracking yielded RMS errors in excess of 10° in the protraction-retraction and anterior-posterior tilt direction. The findings of this study demonstrate high-quality 3D humerothoracic and elbow joint motion measurement capability using IMUs and underscore the challenges of skin motion artifacts in scapulothoracic and glenohumeral joint motion analysis. Future studies ought to implement functional joint axis calibrations, and IMU-based scapula locators to address skin motion artifacts at the scapula, and explore the use of artificial neural networks and data-driven approaches to directly convert IMU data to joint angles.

Emerging Trends of Ergonomic Risk Assessment in Construction Safety Management: A Scientometric Visualization Analysis

Ergonomic risk assessment is critical for identifying working posture hazardous to the health of construction workers. Work-related musculoskeletal disorders (WMSDs) are predominant non-fatal injuries in the construction industry owing to manual handling activities and poor working conditions. However, there is a lack of scientific synopsis aiming to better understand the emerging research focus in this field. To fill the research gap, this study performed a scientometric evaluation of the bibliometric data on ergonomic risk assessment from the Web of Science database using VOSviewer software. The purpose of this study is to analyze the co-occurrence network of keywords, co-authorship network, most active countries, and the sources of publication. The results indicate that research related to risk assessment in construction has fluctuating growth, peaking in 2020 with significant advancements in the USA, China, and Canada. WMSDs, risk factors, construction workers, and ergonomics are hot research topics in this field. Furthermore, the research gaps of previous studies and suggestions for future research have been provided to bridge the knowledge gap. We believe that this scientometric review provides useful reference points for early-stage researchers as well as beneficial in-depth information to experienced practitioners and scholars in the construction industry.

Biomechanics Analysis of the Firefighters' Thorax Movement on Personal Protective Equipment during Lifting Task Using Inertial Measurement Unit Motion Capture

Back injury is a common musculoskeletal injury reported among firefighters (FFs) due to their nature of work and personal protective equipment (PPE). The nature of the work associated with heavy lifting tasks increases FFs' risk of back injury. This study aimed to assess the biomechanics movement of FFs on personal protective equipment during a lifting task. A set of questionnaires was used to identify the prevalence of musculoskeletal pain experienced by FFs. Inertial measurement unit (IMU) motion capture was used in this study to record the body angle deviation and angular acceleration of FFs' thorax extension. The descriptive analysis was used to analyze the relationship between the FFs' age and body mass index with the FFs' thorax movement during the lifting task with PPE and without PPE. Sixty-three percent of FFs reported lower back pain during work, based on the musculoskeletal pain questionnaire. The biomechanics analysis of thorax angle deviation and angular acceleration has shown that using FFs PPE significantly causes restricted movement and limited mobility for the FFs. As regards human factors, the FFs' age influences the angle deviation while wearing PPE and FFs' BMI influences the angular acceleration without wearing PPE during the lifting activity.

A Work-Related Musculoskeletal Disorders (WMSDs) Risk-Assessment System Using a Single-View Pose Estimation Model

Musculoskeletal disorders are an unavoidable occupational health problem. In particular, workers who perform repetitive tasks onsite in the manufacturing industry suffer from musculoskeletal problems. In this paper, we propose a system that evaluates the posture of workers in the manufacturing industry with single-view 3D human pose-estimation that can estimate the posture in 3D using an RGB camera that can easily acquire the posture of a worker in a complex workplace. The proposed system builds a Duckyang-Auto Worker Health Safety Environment (DyWHSE), a manufacturing-industry-specific dataset, to estimate the wrist pose evaluated by the Rapid Limb Upper Assessment (RULA). Additionally, we evaluate the quality of the built DyWHSE dataset using the Human3.6M dataset, and the applicability of the proposed system is verified by comparing it with the evaluation results of the experts. The proposed system provides quantitative assessment guidance for working posture risk assessment, assisting the continuous posture assessment of workers.

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

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.

Added value of motion capture technology for occupational health and safety innovations

Ergonomic principles in production assembly and manufacturing operations have become an essential part of comprehensive health and safety innovations. We aim to provide new insights into occupational health and safety innovations and how they utilise biomechanical methods and cutting-edge motion capture technology by assessing movements at a workplace. The practical goal is to quantify a connection between work exposure and ergonomic risk measures to determine biomechanical risk factors of diseases or health-related disorders objectively. The target group consisted of 62 factory employees working in manufacturing (26 participants on 12 devices) or assembly areas (36 participants on 9 devices). Body posture, body parts position, movements, energy cost and workloads were assessed using an inertial motion capture (MC) system. MC technology accurately assesses the operator’s movements. The proposed methodology could complement ergonomic procedures in the design of workstations, which is the added value of the motion capture technology for occupational health and safety innovations.