Hybridized Hierarchical Deep Convolutional Neural Network for Sports Rehabilitation Exercises

Abstract

In recent years, rehabilitation has become a specialist field after a sports injury, and the evolution of rehabilitation has inevitably brought together a sports physiotherapist, sports doctor, and an orthopedic surgeon. For sports athletics, it is essential to determine the strategies to prevent injuries, optimize the rehabilitation, and improve performance. In the field of computer vision, deep learning has made a great success and the accuracy in image detection and image classification. Computer-aided physical rehabilitation evaluation involves assessing patient performance during the completion of prescribed rehabilitation exercises using sensory system data from the processing of movement. Since the rehabilitation assessment plays a vital role in improving patient outcomes and reducing healthcare costs, existing solutions lack versatility, robustness, and practical relevance. In this article, Hybridized Hierarchical Deep Convolutional Neural Network (HHDCNN) has been introduced to enhance the accuracy, image segmentation of sports athletics exercise rehabilitation. The main components of the framework include measurements to quantify motion performance, scoring of performance measurement features into numerical quality scores, and deep convolutional neural network models to generate quality scores of input movements through supervised learning. Compared to many traditional neural network algorithms, the image segmentation algorithm enhances the convergence speed of the network, shortens training time, and improves the accuracy of sports athletics exercise rehabilitation, which is good practice for the reconstruction of the sports rehabilitation exercise.