SEE: A proactive strategy-centric and deep learning-based ergonomic risk assessment system for risky posture recognition

Abstract

Work-related musculoskeletal disorders (WMSDs) are serious workplace injuries that put workers' safety at risk. However, traditional WMSD assessments are based on the human-evaluation strategy (HES), requiring human intervention. Proactive strategy (PAS)-oriented WMSDs assessments collect data using posture data tags and special semi-human–machine equipment to improve efficiency and reduce human efforts to capture specific postures in a real-world setting. Meanwhile, more research on applying artificial intelligence-based pose machines for musculoskeletal risk assessment in various workplaces is needed. Hence, this study proposed a holistic posture acquisition and ergonomic risk analysis model with the PAS-oriented philosophy for developing a smartphone-based and workplace-based risk assessment system for WMSDs. The Convolutional Pose Machines (CPM) method was combined with a rapid entire body assessment method for the system's design. Finally, the smart ergonomic explorer (SEE) system includes three subsystems: an automotive scene capturer, an ergonomic risk level calculator, and a risk assessment reporter. A musculoskeletal risk assessment experiment with 13 poses was also carried out to validate the SEE system and compare its accuracy with manual evaluation. The result shows good agreement with the REBA score, with an average proportion agreement index (P0) of 0.962 and kappa of 0.82. It indicates that the proposed system can not only accurately analyze the working posture, but also accurately evaluate the total REBA scores. This study is hoped to provide practical advice and implications for achieving a more effective empirical response for WMSD assessment.