Human Pose Recognition System Design Based on Star Skeleton Feature Applied to Self-Rehabilitation
|關鍵字:||姿態辨識;居家復健;類神經;星狀骨骼;pose recognition;self rehabilitation;neural network;star skeleton|
In recent years, many kinds of recovery auxiliary equipments have been developed in medical field to fulfill the patients’ needs for rehabilitation and security. Human pose recognition system which could automatically supervise patients’ motion has received increasing attention. This thesis proposes a human pose recognition system to help those patients follow the rehabilitation program and supervise their safety. The system is composed of three parts, including human image detection, feature extraction and human pose recognition. For human image detection, it is implemented by background subtraction, edge detection and connected component labeling (CCL). The feature extraction is then processed by the star skeleton method, and the human pose recognition is fulfilled by the neural network classifiers designed by the back propagation algorithm. From the experimental results, the proposed system is able to recognize a human pose in 0.2 second with accuracy 92.39%, which could be applied in real time system.