Digitalization of Cross-Country Skiing Training Based on Multisensor Combination

Abstract

The status and role of science and technology in the field of modern competitive sports have become increasingly prominent. The construction of a scientific training command system is of great significance for improving the scientific level of the training process and deepening the digital cognition of ski training. This paper is based on the multisensor combination to conduct a digital research on cross-country skiing training, aiming to conduct in-depth research on the realization of human motion capture and the theory of motion inertial sensing. To build a scientific, formal, and malleable ski training program, the requirements for data acquisition, recording, and analysis are quite strict. For this, it is necessary to use scientific and reasonable tools combined with multiple algorithms to process information and data. During the experiment, accelerometers, gyroscopes, and magnetometers are selected as sensors to receive motion information, and recognition algorithms for identifying weightlessness, hybrid filtering algorithm, displacement estimation algorithm, and kinematic principles are adapted to process multisensor data using information integration technology. A human body motion model was established based on kinematic principles, and a cross-country skiing motion measurement program was designed. The experimental results show that, according to the combination of multisensing and video platform, the athlete’s posture prediction is adjusted, and the action on the track is more consistent, which can accelerate the athlete’s skiing speed and the size of the inclination angle to a large extent. It can affect the direction of the athlete’s borrowing force and the adjustment of gravity during the exercise. The tilt angle is expanded from 135° to 170°, and it can maintain good continuity during the exercise.