Application of Motion Image Skeleton Recognition Algorithm Based on Convolutional Neural Network in Rehabilitation Training
Description
This paper aims to explore the application effect of Convolutional Neural Network (CNN) algorithm in motion image skeleton recognition to improve the accuracy of motion skeleton recognition and the efficiency of rehabilitation training. Here, in response to the robustness and insensitivity to changes in lighting conditions of human motion image skeleton data, the first step is to depict human motion images in real Three-Dimensional (3D) space by abstracting the human body into several joint points connected to bones to obtain 3D skeleton data. Second, the channel attention module is proposed, which is combined with the spatial graph convolution module and the time graph convolution module in the graph CNN. The 3D skeleton data samples of diverse motion images are learned to obtain good action expression ability. A motion image skeleton recognition model based on multi-attention Spatial Temporal Graph Convolutional Network is constructed. Finally, the constructed model is applied to the tracking and analysis of rehabilitation training. The results show that the model algorithm can track the patient's motor state accurately in rehabilitation training. The effectiveness of local and global information reaches more than 90%, which is obviously better than the spatiotemporal graph convolution algorithm. The model algorithm effectively reduces the problem of muscle injury caused by exercise posture error during manual intervention in traditional rehabilitation training. Therefore, the model algorithm can be widely used in rehabilitation training and practice, which provides new ideas and exploration directions for the medical industry to improve the efficiency and quality of rehabilitation training.