Analysis and optimization of athlete performance based on deep learning

Zhibo Feng; Fei Huang

doi:10.1117/12.3049235

17 October 2024 Analysis and optimization of athlete performance based on deep learning

Zhibo Feng, Fei Huang

Author Affiliations +

Proceedings Volume 13289, International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024); 132890S (2024) https://doi.org/10.1117/12.3049235
Event: The International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024), 2024, Hangzhou, China

Abstract

The application of deep learning technology in the analysis and optimization of athlete performance has garnered increasing attention. This paper reviews the research progress of deep learning in areas such as motion capture and recognition, physical fitness and health monitoring, competition data analysis, training plan generation and adjustment, movement and tactical improvement, as well as injury prevention and recovery. Through experimental studies, deep learning models have demonstrated the ability to automatically extract key features from massive sports data, generate intelligent analysis results, and provide optimization suggestions, thus offering robust technical support for athletes and coaches. Despite facing challenges in applying deep learning to practical sports scenarios, these research findings have opened up new directions for sports science research. In the future, deep learning technology is expected to continue playing an important role in the field of sports, promoting the scientific and modern development of sports.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhibo Feng and Fei Huang "Analysis and optimization of athlete performance based on deep learning", Proc. SPIE 13289, International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024), 132890S (17 October 2024); https://doi.org/10.1117/12.3049235

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