Bridging Exercise Monitoring System Using RGB Camera for Stroke Rehabilitation

Bridging exercise is a widely applied training for stroke rehabilitation to improve balancing ability on weight-bearing activities. Aiming to reduce the workload of physical therapists and enable the systematic recording of motion data, this paper presents an affordable rehabilitation monitoring system using an RGB camera. For predicting the correctness of the bridge posture, the MediaPipe framework is applied for detecting the human body segments which are used as the input data of the decision tree classifier instead of using a complex neural network. The model was trained using the data collected from five healthy participants performing the correct and Wide Knee postures when the knees are separated laterally. The experimental results show that nearly 100 percent accuracy can be achieved in confirming the correct posture and identifying the Wide Knee posture. The time performance of the decision tree classifier trained by the different number of frames is also evaluated. This system is very promising to help therapists monitor patients and provide feedback for improving the effectiveness of the rehabilitation.