||Research on 3D Human Motion Capture Algorithm for Online Physical Education Teaching
||(Weiguo Li) ; (Yongli Yang) ; (Jing Zhou) ; (Zhipeng Li)
|| Online sports course; Gradient descent method; Motion capture system; Data fusion
||This study proposes to use the gradient descent method in data fusion to solve the problem of low learning efficiency of online sports courses by optimizing the attitude calculation and combining gait detection and zero speed correction to form a three-dimensional human motion capture system. The system was applied to online physical education teaching to improve teaching quality. The performance of the optimized attitude calculation algorithm and the Kalman filter algorithm was compared. The results show that the error angle of the data-fusion-attitude calculation algorithm was 1.67°, which is better than the Kalman filter algorithm. In the empirical analysis of the 3D human motion capture system designed by the research, the system could correctly capture the real human motion trajectory. Moreover, comparative analysis of the online physical education courses showed that the satisfaction of the online physical education courses embedded in the system was 90.62, which was much higher than other online physical education courses. These results show that the optimized attitude calculation algorithm can better capture real human movements, and the human motion capture system composed of this can improve the learning effect of online physical education courses. This method opens up new ideas for online physical education teaching.