Risk assessment for musculoskeletal disorders based on the characteristics of work posture

Abstract

Construction workers are at high risk of work-related musculoskeletal disorders (WMSDs). Although the existing observational assessment methods are easy to use, when it comes to a more in-depth statistical analysis of the dynamic characteristics of the worker's operation, the sample data to be processed turn out to be large, the labor cost high, and the analysis easily affected by the prejudice of the evaluator. This study examines a novel WMSD prediction method based on the dynamic characteristics of the working posture, which comprises three artificial intelligence algorithms in series. In this method, the posture detector identifies the limb angles and state in the working video, the posture risk evaluator evaluates the risk level of the working posture frame by frame, and the task risk predictor predicts the risk level of the current work process. The collected video data of common tasks of construction workers and the MPII Human Pose dataset were used for training and evaluation of the algorithms. The method achieved 87.0% accuracy of the joint point recognition. The micro-averaged accuracy, recall, and F1-score (harmonic average of accuracy and recall) reached 96.7%, 96.0%, and 96.6%, respectively. The results showed that the proposed method has great potential for real-time risk assessment. It can output all of the changes of the limb angles of workers in the work process frame by frame and predict the risk level of the whole work process.