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Optical wearable sensor based player injury detection with movement analysis using hybrid machine learning model

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Abstract

The field of medical monitoring might greatly benefit from development of wearable optical fibre sensors. Development of wearable optical fibre sensors is progressively satisfying the need for new medical monitoring devices to be more small, comfortable, accurate, and have other capabilities. Since the idea of high-quality education was introduced, society has paid close attention to the athletic endeavours of pupils. Therefore, it is an irreversible tendency to identify sports injuries. Sports injuries among students during sports have also raised widespread concern. Purpose of this project is to use ML approaches to provide player injury and mobility assessments based on optical sensors. Here, the player’s movement and the injury are analysed by the optical wearable sensor. Then this sensed data is classified using hybrid vector adversarial reinforcement perceptron neural networks (HVARPNN). The experimental analysis is carried out in terms of prediction accuracy, precision, MSE, AUC and F-1 score for various player analysis based dataset. The validation findings show that the system can reliably detect collisions with extremely few false positives and false negatives.

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Authors and Affiliations

  1. School of Physical Education and Art, Jiangxi University of Science and Technology, Ganzhou, 341000, China

    Ke Ni

  2. Department of Physical Education, Shenzhen Polytechnic University, Shenzhen, 518055, China

    Hailong Li

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  1. Ke Ni
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  2. Hailong Li
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Contributions

KN: Conceived and design the analysis, Writing--Original draft preparation. Collecting the Data, HL: Contributed data and analysis stools, Performed and analysis, Performed and analysis, Wrote the Paper,  Editing and Figure Design.

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Correspondence to Hailong Li.

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Ni, K., Li, H. Optical wearable sensor based player injury detection with movement analysis using hybrid machine learning model. Opt Quant Electron 56, 421 (2024). https://doi.org/10.1007/s11082-023-06077-w

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