The current technology related to athlete gait recognition has shortcomings such as complicated equipment and high cost, and there are also certain problems in recognition accuracy and recognition efficiency. In order to improve the efficiency of athletes’ gait recognition, this paper studies the different recognition technologies of athletes based on machine learning and spectral feature technology and applies computer vision technology to sports. Moreover, according to the calf angular velocity signal, the occurrence of leg movement is detected in real time, and the gait cycle is accurately divided to reduce the influence of the signal unrelated to the behavior on the recognition process. In addition, this study proposes a gait behavior recognition method based on event-driven strategies. This method uses a gyroscope as the main sensor and uses a wearable sensor node to collect the angular velocity signals of the legs and waist. In addition, this study analyzes the performance of the algorithm proposed by this paper through experimental research. The comparison results show that the method proposed by this paper has improved the number of recognition action types and accuracy and has certain advantages from the perspective of computation and scalability.
Selfgait: A Spatiotemporal Representation Learning Method for Self-Supervised Gait Recognition