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.
Highlights
Cross-country skiing is a weak item of winter sports in my country [1]
Based on the camera projection model, the sensor characteristics are analyzed, the error and accuracy of the sensor are measured through experiments, and the image registration model under the condition of introducing the camera rotation angle information is derived by using the transformation relationship of the camera movement
The use of sensor data to identify the human body will continue to flourish with the development of sensor technology: as the energy consumption of the sensor decreases, the volume becomes smaller, the sensitivity increases, and the accuracy improves; as more algorithms are proposed and introduced, I believe this subject direction can have more research and applications on various human actions, various human behaviors, and even more people-to-human interaction occasions
Summary
Cross-country skiing is a weak item of winter sports in my country [1]. Injuries of skiers during training and various strains caused by long-term training are often reported. Flexibility and accumulation of physical movement information in this program are a prerequisite for the completion of functional identification This can provide data support and work support for scientific training and combine the specific needs of coaches and athletes to improve the shallow training plan in the traditional national ski training process. This paper adopts a weight recognition algorithm, hybrid processing algorithm, motion simulation algorithm, kinematic process, etc., uses information synthesis technology to process multidimensional data, further improves the scientific level of process training, and solves the limitations of traditional training procedures This methodology is developed, a standardized model of ski music is used, and multisensor integration functions such as position information, video processing, and data analysis are used to determine the athlete’s strength tracking and a set of scientific, comprehensive, and practical cross-country skiing training teaching plans
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