SEE: A proactive strategy-centric and deep learning-based ergonomic risk assessment system for risky posture recognition |
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| Affiliation: | 1. College of Management and Design, Ming Chi University of Technology, Taiwan;2. School of Public Policy and Administration, Xi’an Jiaotong University, China;1. Faculty of Engineering, Universidad de La Sabana, Chía, Colombia;2. Faculty of Mechanical Engineering, Escuela Politécnica Nacional, Quito, Ecuador;3. Instituto de Biomecánica de Valencia, Universidad Politécnica de Valencia, Valencia, Spain;1. Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai 600124, India;2. Department of Mechanical Engineering, Chennai Institute of Technology, Chennai 600069, India;1. State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China;2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China;3. Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, Zhejiang University, Hangzhou 310027, China;1. University of Sannio – Department of Engineering, Benevento, Italy;2. University of Padua – Department of Technic and Industrial Systems Management, Padua, Italy;3. Merisol Srl, Ariano Irpino, Italy;1. Department of Construction Science, Texas A&M University, 3137 TAMU, College Station, TX 77843, USA;2. Department of Computer Science, Texas A&M University, 3112 TAMU, College Station, TX 77843, USA |
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| 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. |
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| Keywords: | Posture acquisition Risk analysis Rapid Entire Body Assessment (REBA) Convolutional Pose Machines (CPM) Proactive Strategy (PAS) Deep learning Work-related Musculoskeletal Disorders (WMSDs) |
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