Sanda training program based on multi‐sensor data fusion in the internet of things environment

Abstract

Sanda, also known as Chinese kickboxing, is a Wushu that combines elements of traditional Kung Fu, kickboxing, and grappling. Considering that the use of action recognition can effectively improve the level of Sanda players, this study uses inertial sensors and visual sensors to collect the motion data of Sanda. A federated Kalman filter with time‐varying parameters is designed according to the error form of each sensor to complete the data fusion. The main idea is that the whole system is divided into several local filtering systems, and a primary filter is designed for these systems. The main structure of the filter is a cascaded decentralized filtering structure composed of several local filters and a primary filter. The experimental results show that the proposed Sanda training scheme based on multi‐sensor data fusion under the Internet of Things environment has high action recognition accuracy, and the joint inertial‐vision model produces significantly better results than the other two models.