Abstract
The traditional basketball training monitoring method has some limitations, such as relying on manual judgment and subjective evaluation, and being limited by equipment and environmental conditions. In order to solve these problems, an optical sensor system is designed and constructed in this study, which can capture the optical signals during basketball training in real time and convert them into digital data. These data are analyzed and processed using deep learning algorithms to extract features related to basketball training. A large number of experiments and tests have verified the effectiveness and accuracy of this optical sensor method. The experimental results show that this method can monitor and record the key information of basketball movement track, players’ movements and skills in real time, and can quantitatively evaluate the quality and effect of basketball training. Through the combination of deep learning technology and optical sensors, non-invasive, high-precision and real-time monitoring can be achieved, which provides a powerful auxiliary and evaluation means for the training of basketball players, and helps to improve the training effect and level.
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No datasets were generated or analysed during the current study. The data will be available upon request.
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BZ has done the first version, JL has done the simulations. All authors have contributed to the paper’s analysis, discussion, writing, and revision.
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Zhao, B., Liu, J. Research on the application of light detection sensors based on deep learning in basketball training monitoring. Opt Quant Electron 56, 558 (2024). https://doi.org/10.1007/s11082-023-06242-1
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DOI: https://doi.org/10.1007/s11082-023-06242-1