Abstract
Predicting future performance is an important part of research into sports' future growth methods. Physiological and biochemical data may now be monitored in real time thanks to state-of-the-art wearable sensors. This research offers a novel approach, based on machine learning and an optical quantum model, to improving player wearable sensor-based sports performance. Here, data on player efficiency was collected and evaluated by optically-mounted sensors. After that, component vector multilayer transfer modelling is used to identify and classify data features using a quantum modelling based adversarial encoder neural network. Experimental analysis is performed for a number of sports performance datasets using metrics including accuracy, precision, MSE, area under the curve, and F-1 score for making predictions. The wearable solution was designed, built, calibrated, and tested in a laboratory environment, where it was shown to be accurate in tracking the expected pattern of an ankle plantar-dorsi-flexion movement during gait. Prediction accuracy was 95%, precision was 88%, MSE was 51%, area under the curve was 81%, and the F-1 score was 85% using the proposed method.
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Feng Du—Conceived and design the analysis; Writing-Original draft preparation; Collecting the Data; Contributed data and analysis stools; Performed and analysis, Performed and analysis; Wrote the Paper; Editing and Figure Design.
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Du, F. Enhancing sports performance through quantum-based wearable health monitoring data analysis using machine learning. Opt Quant Electron 56, 250 (2024). https://doi.org/10.1007/s11082-023-05800-x
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DOI: https://doi.org/10.1007/s11082-023-05800-x