Research on Positioning Tracking Mode of Sports Rehabilitation Training Based on Self-Powered Sensor Based on Particle Swarm Optimization Algorithm

Abstract

The theme of today’s world is peace and development. A stable external environment has made people’s average life expectancy gradually increased, and the world is rapidly aging. Aging has brought many problems, such as the increase in the number of patients with limb dysfunction due to various diseases, which has gradually increased the demand for rehabilitation training. With traditional rehabilitation training methods, the training scenes are single and boring, and patients are prone to resisting. This paper designs and implements a real-time rehabilitation training guidance system based on self-powered sensors for the rehabilitation training needs of stroke patients. The system uses self-powered sensors to collect human motion information in real time, and compares it with the key posture sequences in the standard motion library to obtain corresponding matching results and guide patients to perform correct rehabilitation training. Using the rotation quaternion of 25 bone points in the patient’s rehabilitation exercise to calculate and update the rotation quaternion of the corresponding bone point of the character model, the function of the character model to follow the patient’s mirror motion is realized. This allows patients to control the completion of their rehabilitation movements without the need for medical staff to accompany them. And the stability of the system is optimized based on the particle swarm optimization algorithm. After traversal optimization, the current sensitivity coefficient of the model is reduced by about 75% compared with that before the correction, indicating that the current stability of the model obtained at this time has been improved to a certain extent. However, in the regression model of the self-powered sensor established by the particle swarm optimization algorithm, its parameters are reduced by about 82% compared with those before the correction, which shows that the current stability of the model has been greatly improved at this time, and the operating current of the receiving loop has been greatly improved.