Effective real-time self-rehabilitation exercise monitoring and correctness system for low back pain management.

In the modern era of working, Musculoskeletal Disorders (MSDs) are increasing drastically. One of the leading causes of MSD is Low Back Pain (LBP). Patient health monitoring technology is paramount to the investigators, enabling remote recovery services via cutting-edge technologies that lower the barrier between clinicians and patients. This work provides a low-cost, efficient, and user-friendly visual capture recovery system for the administration of Low Back Pain (LBP). This study proposes a unique computer vision and deep learning method for remotely monitoring patients' joint angles during physiotherapy rehabilitation. A single long-short term memory layer with 64-unit lightweight model with dense neurons was used to identify the correct postures for LBP recovery exercises in real-time video. The proposed system exploits a 3D human skeleton representation for calculating angles on three landmarks to recognize the angle deviations and classify the nine LBP recuperation exercise poses with high cross-validation accuracy, low computational cost, real-time exercise correction feedback, and minimal latency to process frames. The suggested approach successfully predicts and provides feedback on LBP exercise postures from real-time video feeds captured by common RGB cameras, without additional hardware or specialist cameras, thereby improving the quality of life for people around the globe.