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

Mark White (), Beatrice De Lazzari, Neil Bezodis and Valentina Camomilla
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Mark White: Applied Sports, Technology, Exercise and Medicine (A-STEM) Research Centre, Faculty of Science and Engineering, Swansea University, Swansea SA2 8PP, UK
Beatrice De Lazzari: Department of Movement, Human and Health Science, University of Rome “Foro Italico”, 00135 Rome, Italy
Neil Bezodis: Applied Sports, Technology, Exercise and Medicine (A-STEM) Research Centre, Faculty of Science and Engineering, Swansea University, Swansea SA2 8PP, UK
Valentina Camomilla: Department of Movement, Human and Health Science, University of Rome “Foro Italico”, 00135 Rome, Italy

Mathematics, 2024, vol. 12, issue 12, 1-30

Abstract: Wearable sensors have become increasingly popular for assessing athletic performance, but the optimal methods for processing and analyzing the data remain unclear. This study investigates the efficacy of discrete and continuous feature-extraction methods, separately and in combination, for modeling 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 generalizable 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 optimizing training and enhancing athletic outcomes across various sports disciplines.

Keywords: accelerometer; countermovement jump; feature extraction; functional principal component analysis; inertial measurement units; jump power; signal alignment; smartphone; sport; wearables (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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