Feature Extraction and Personalized Sports Training for Athletes Using Variational Autoencoder (VAE)

Wen, Nan; Diliya, Dilixiati

doi:10.1145/3662739.3672309

2024 International Conference on Machine Intelligence and Digital Applications 2024

DOI: 10.1145/3662739.3672309

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Feature Extraction and Personalized Sports Training for Athletes Using Variational Autoencoder (VAE)

Nan Wen,

Dilixiati Diliya

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2024

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Cited by 1 publication

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A Study of Data-Driven Optimization of Personalized Instructional Strategies for Physical Education Training

Shi

2024

Applied Mathematics and Nonlinear Sciences

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As a new type of sports training, the application of sports information technology can effectively improve the athletes’ sports level and sports performance. In this paper, based on the way and effect of sports information technology application, a system for sports teaching and training assistance is designed. Through sports training intensity monitoring, the training intensity of the athletes is obtained, and a high load alarm is triggered. At the same time, the improved hybrid Kalman filter is used to solve the posture of training movements, and the DTW algorithm is used to recognize the training movements and compare them with the standard movements to obtain the training scores. Coaches can be assisted in formulating personalized teaching strategies by using the system’s obtained sports training data. This paper’s sports teaching and training assistance system has a significantly shorter response time than the two comparative systems, which suggests it has better response performance and practicality. The RMSE values of the improved hybrid Kalman filter are 0.92, 0.84 and 1.47 for the roll, pitch and yaw angles, respectively, which are better than that of the extended Kalman filter, indicating that it is closer to the original data and has better data fusion performance. Compared with the control class using traditional teaching strategies, the students in the experimental class using the personalized teaching strategies of this paper have higher skill improvement, indicating that the strategies of this paper have an important guiding role for personalized physical education teaching.

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A Study of Data-Driven Optimization of Personalized Instructional Strategies for Physical Education Training

Shi

2024

Applied Mathematics and Nonlinear Sciences

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Feature Extraction and Personalized Sports Training for Athletes Using Variational Autoencoder (VAE)

Cited by 1 publication

References 15 publications

A Study of Data-Driven Optimization of Personalized Instructional Strategies for Physical Education Training

A Study of Data-Driven Optimization of Personalized Instructional Strategies for Physical Education Training

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