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Wearable Sensors for Athletic Performance: A Comparison of Discrete and Continuous Feature Extraction Methods for Prediction Models

Mark White, Beatrice De Lazzari, Neil Bezodis Orcid Logo, Valentina Camomilla

Mathematics

Swansea University Authors: Mark White, Neil Bezodis Orcid Logo

DOI (Published version): 10.3390/math12121853

Abstract

Wearable sensors have become increasingly popular for assessing athletic performance, but the optimal methods for processing and analysing the data remain unclear. This study investigates the efficacy of discrete and continuous feature extraction methods, separately and in combination, for modelling...

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Published in: Mathematics
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URI: https://cronfa.swan.ac.uk/Record/cronfa66702
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Abstract: Wearable sensors have become increasingly popular for assessing athletic performance, but the optimal methods for processing and analysing the data remain unclear. This study investigates the efficacy of discrete and continuous feature extraction methods, separately and in combination, for modelling countermovement jump performance using wearable sensor data. We demonstrate that continuous features, particularly those derived from Functional Principal Component Analysis, outperform discrete features in terms of model performance, robustness to variations in data distribution and volume, and consistency across different datasets. Our findings underscore the importance of methodological choices, such as signal type, alignment methods, and model selection, in developing accurate and generalisable predictive models. We also highlight the potential pitfalls of relying solely on domain knowledge for feature selection and the benefits of data-driven approaches.Furthermore, we discuss the implications of our findings for the broader field of sports biomechanics and provide practical recommendations for researchers and practitioners aiming to leverage wearable sensor data for athletic performance assessment. Our results contribute to the development of more reliable and widely applicable predictive models, facilitating the use of wearable technology for optimising training and enhancing athletic outcomes across various sports disciplines.
College: Faculty of Science and Engineering
Funders: This research was funded by Regione Lazio, Call: POR FESR Lazio 2014–2020 (Azione 1.2.1), grant number 20028AP000000095. 1.2.1), grant number 20028AP000000095. The APC was waived.

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