Digital sports training based on digital video image processing promises to reduce the reliance on the experience of coaches in the table tennis training process and to achieve a more general physical education base. Based on this approach, this paper describes the specific forms of exercise content, movement characteristics, and skill levels in the table tennis framework and specifies the calculation methods of motion capture and movement characteristics suitable for table tennis. Meanwhile, to further improve the accuracy of the inertial motion capture system in restoring the position posture of the trainees, this paper improves the original inertial motion capture system from two aspects: contact judgment of both feet and correction of the position posture based on the contact position constraint. The simulation results show that the corrected human posture has good action smoothness. This paper first proposes a knowledge-based generic sports-assisted training framework based on generalizing the traditional sports training model. The framework contains four main modules: domain knowledge, trainees, sport evaluation, and controller. The domain knowledge module is a digital representation of the knowledge of the exercise content, improvement instructions, and skill indicators of the sport; the trainee module is the active response of the trainee to the exercise content and improvement instructions; the motion evaluation module uses motion capture technology to obtain the raw motion data of the trainee and further calculates the motion characteristics; the controller module proposes improvement instructions to the trainee or makes him/her practice new content based on the results of the motion evaluation. Based on the results of the motion evaluation, the controller module proposes improvement instructions or makes the trainee practice new content until the trainee achieves the desired goal.
Human motion capture system in sports performance based on Internet of Things technology and wireless inertial sensor
