Implementation of a Deep Learning-Based Application for Work-Related Musculoskeletal Disorders’ Classification in Occupational Medicine

This research aims to develop an AI-based Ergonomics risk hazard posture recognition system to help reduce the risk of injury to workers and improve work safety in factories and warehouses. The background shows that ergonomic risk hazards are one of the most important risk factors in the workplace, among which the risk of posture hazards is higher when the human body is carrying objects. Otherwise, KIM-LHC (Key Indicator Methods - Lifting/Holding/Carrying) was used as the basis for posture determination, and the human posture information was converted into data by Movenet, and then build the neural network classification model used to recognize and analysis human pose, finally integrated into the app built by flutter. The app built by Flutter is finally integrated. In order to verify the performance of the system, it conducted experiments by actual video recording, and the results showed that the verification accuracy of the app could reach over 97%, and successfully identified the dangerous postures that might cause injury risks to workers, and the app was easy to understand and practical. In summary, this research developed an AI-based Ergonomics risk-hazard posture recognition system, which is important for improving workplace safety.