Abstract
Walking may seem simple, but it actually involves complex control processes. Walking is accomplished through a series of collaborative operations, including coordinated control, balance control, central command, and various other physiological mechanisms. When problems arise between these links, it may cause abnormal gait or motor injury. Gait analysis of athletes can help coaches and medical personnel evaluate their athletic skills and physical health. Therefore, this article aims to develop an effective athlete gait analysis method based on fiber optic sensors and computer vision algorithms. Fiber optic sensors capture subtle changes in athletes' gait by measuring the changes in optical signals in the fiber optic. The collected gait data includes parameters such as stride length, stride frequency, and gait phase. Step length refers to the distance traveled during a walk, providing detailed information about an athlete's gait and helping to evaluate their athletic skills and physical health. Using computer vision algorithms to process and analyze the collected gait data, accurate gait parameters are obtained for identifying athletes' walking patterns and identifying abnormal gait.