Real-time AI-assisted visual exercise pose correctness during rehabilitation training for musculoskeletal disorder

Exercise therapy is a prominent method for recovering from musculoskeletal disorders like Work-related Musculoskeletal Disorder (WMSD), Classroom-related Musculoskeletal Disorder (CMSD). This method requires the monitoring of a physiotherapist to advise the performer to do the exercise poses correctly. This process is costly and requires the presence of a physiotherapist. In this work, we plan to develop software for the real-time detection of exercise poses with the help of AI. This work replaces the physiotherapist and provides good real-time feedback on the users' wrist extension and flexion exercise posture. Artificial Intelligence (AI) is the most popular and widely used technique for real-time image analysis. In real-time the user takes at least 1 s to perform the exercise pose. Our model classifies and provides feedback within 0.79 s. The frame processing rate of our model is similar to 21 frames per second. Experimentation of this framework was done through CNN DenseNet with 2-level architecture. We have experimented with three different ways of outcomes. Our mode achieved 100% accuracy with 106 samples of data and 99.86% accuracy with 2160 samples. Finally, with 144 cross-person sample datasets achieved 83.33% accuracy. This technique performs well for evaluating wrist extension exercise poses for recovery and gives participants immediate feedback on whether their wrist extension is correct or incorrect.