Development and validation of an integrative, automated ergonomic assessment system for preventing work-related musculoskeletal disorders among heavy industry workers

Abstract

Work-related musculoskeletal disorders (WMSDs) are one of the most critical occupational health problems and the leading cause of worker injuries and compensable claims in the heavy industry. Although different WMSDs risk assessment methods have been developed, it is difficult for the industry to directly apply them for practical applications because of difficulty on reliable real-time in-situ data acquisition due to work dynamics, complex data processing/analytics, and the need of ergonomics experts. Besides, most of the existing WMSDs risk assessment systems used limited risk assessment methods hence could only provide limited information about the WMSDs risk for the target task. Therefore, it is necessary to develop an automated and integrative system for evaluating WMSDs risk with different assessment methods, which could provide comprehensive assessment results in different levels (e.g., global level, skeletal joint level, in-vivo muscle level) automatically and systematically. The primary goal of this research is to develop and validate an automated and integrative WMSDs risk assessment system, utilizing wearable IMU motion sensors for the practical applications in the heavy industry. Three different WMSDs risk assessment tools were automatized and integrated into one system based on the kinematic information of the human body collected from a wearable IMU motion capture system (Xsens). The first tool could conduct the postural ergonomic analysis to assess the overall WMSDs risk by automatizing RULA/REBA methods based on human body joint angle data. The second tool could perform static biomechanical analysis for estimating low back compression force and the loads at the skeletal joints using human body segment position data. And the third tool could perform dynamic biomechanical analysis to predict the in-vivo muscle activity through the motion-driven musculoskeletal simulation. The effectiveness of the proposed system was then evaluated through two follow-up validation experiments. The first experiment was carried out to validate the postural ergonomic analysis and static biomechanical analysis tools by comparing the results from the developed system and selected references (e.g. expert rating, widely used commercial software), in which three common types of jobs (in total 16 job tasks) in heavy industry were performed by 20 young subjects. The experimental results showed that the developed system achieved high consistency with expert raters for postural ergonomic analysis (intraclass correlation coefficient (ICC) > 0.8, classification accuracy > 88%). For the static biomechanical analysis, the developed system also achieved high consistency with the reference system (3DSSPP): mean intersystem coefficient of multiple correlation (CMC) > 0.896 and relative error <9.5%. The second experiment was conducted to validate the proposed motion-driven dynamic biomechanical analysis tool. In this experiment, 5 subjects performed 3 heavy industry tasks and their actual muscle activities were measured from electromyography (EMG) sensors, which were compared with the predicted muscle activities from the motion-driven dynamic biomechanics model. The results showed similar muscle activity patterns and the moderate association between predicted and measured muscle activities (RMSE: 12.2% ± 9.2%

CMC: 0.497 ± 0.183), indicating the proposed dynamic biomechanical analysis using composite muscle recruitment criterion was capable of estimating in-vivo muscle activities for the detailed assessment of WMSDs risks in common heavy industry tasks. In all, an integrated automated ergonomic assessment system has been developed and validated to assess risks of WMSDs for practical applications in the heavy industry. The developed system can not only assess the overall WMSDs risk levels but also provide detailed information to enhance the understanding of underlying mechanisms of WMSDs and to improve occupational safety in the heavy industry.