Human pose recognition and automated scoring detection for sports rehabilitation

Abstract With the development of sports rehabilitation, accurate assessment of the patient’s rehabilitation process has become the key to enhance the rehabilitation effect. To solve the problems of inaccurate recognition and poor real-time performance of the rehabilitation human pose recognition model for traditional sports in complex environments, this study proposes an integrated framework for efficient and accurate human pose recognition and automated scoring in sports rehabilitation. The study constructs a human pose recognition model using a human pose tracking algorithm and achieves pose classification by extracting key points of the human skeleton and combining them with a random forest algorithm. Meanwhile, a siamese neural network and similarity metric algorithm are introduced to optimize the automated score detection model, accurately assessing the quality of rehabilitation movements. The outcomes indicated that the automatic scoring detection system achieved 98% accuracy in human body pose recognition. In terms of joint angle error, the error rate of the detection model designed in the study was below 6%, which was significantly better than the comparison method. In rehabilitation score correlation test, the correlation of the model was maintained at 92–98%, demonstrating higher scoring accuracy. The outcomes reveal that the model designed in the study has high recognition accuracy and evaluation stability. This makes it an efficient and accurate assessment tool for rehabilitation therapy. It can also effectively improve the effectiveness of rehabilitation training and the quality of life of patients.