Research on basketball players’ action recognition based on interactive system and machine learning

Abstract

The difficulty of sports gesture recognition is the effective cooperation of hardware and software. Moreover, there are few studies on machine learning in the capture of the details of sports athletes’ gesture recognition. Therefore, based on the learning technology, this study uses the sensor with gesture recognition algorithm to analyze the detailed motion capture of sports athletes. At the same time, this study selects inertial sensor technology as the gesture recognition hardware through comparative analysis. In addition, by analyzing the actual needs of athletes’ gesture recognition, the Kalman filter algorithm is used to solve the athlete’s posture, construct a virtual human body model, and perform sub-regional processing, so as to facilitate the effective identification of different limbs. Finally, in order to verify the validity of the algorithm model, the basketball exercise is taken as an example for experimental analysis. The research results show that the basketball gesture recognition method used in this paper is quite satisfactory.