GAIT ANALYSIS AND DEEP LEARNING FOR OVERUSE INJURY DETECTION IN OUTSTANDING DISTANCE RUNNERS

Abstract

Injuries caused by overuse are common in long-distance runners, and early detection of overuse-induced injuries can assist coaches in adjusting training programs to avoid further development of injuries and effectively prevent serious injuries. Gait research is an important tool in distance running research, through the athlete's gait parameters can obtain the athlete's movement status and injury. However, the traditional less research requires rich experience guidance, which is not conducive to widespread promotion. In this paper, deep learning technology is utilized to construct an athlete overuse injury gait detection model. Through the automatic analysis of the athletes less parameters to detect whether there is overuse-induced injury, early detection of injury trends, to avoid injury aggravation. Through experiments, it is verified that the model can effectively identify the gait parameter characteristics of overuse injury in excellent athletes.