IoT Networks Assisted Baduanjin Auxiliary Training System Based on Depth Camera

Abstract

Baduanjin auxiliary training system has insufficient feature definition for similar actions, which affects the recognition accuracy of effective action data. A depth camera-based Baduanjin auxiliary training system is designed. The depth camera is used as the core to design the peripheral circuit and develop the relevant firmware in the hardware part. TPS2552 is used to design the USB current limiting protection circuit, and a CrossLink chip is used to realize the interface and level conversion. In the software part, the human body 3D model of Baduanjin training is generated and the parameters of the human body model are obtained by establishing the mapping relationship between the model and the human body features in the image. Based on the automatic calibration of Baduanjin motion feature points by the depth camera, the motion posture was analyzed and summarized, the attitude auxiliary indicators were established, and the posture standard was judged by comparing the distance of the posture joints in the reconstructed 3D human body model. The system test results show that the recognition accuracy of Baduanjin auxiliary training system based on the depth camera is 96.25% for effective action data, which is 11.53%, 15.28%, and 13.06% higher than that of Kinect, motion intervention, and behavior recognition auxiliary training system, respectively. Therefore, the system can provide analysis and guidance for Baduanjin movement.