Perspective on “in the wild” movement analysis using machine learning

Dorschky E, Camomilla V, Davis J, Federolf P, Reenalda J, Koelewijn A (2023)

Publication Type: Journal article, Original article

Publication year: 2023

Journal

Human Movement Science Elsevier

Book Volume: 87

Article Number: 103042

DOI: 10.1016/j.humov.2022.103042

Abstract

Recent advances in wearable sensing and machine learning have created ample opportunities for “in the wild” movement analysis in sports, since the combination of both enables real-time feedback to be provided to athletes and coaches, as well as long-term monitoring of movements. The potential for real-time feedback is useful for performance enhancement or technique analysis, and can be achieved by training efficient models and implementing them on dedicated hardware. Long-term monitoring of movement can be used for injury prevention, among others. Such applications are often enabled by training a machine learned model from large datasets that have been collected using wearable sensors. Therefore, in this perspective paper, we provide an overview of approaches for studies that aim to analyze sports movement “in the wild” using wearable sensors and machine learning. First, we discuss how a measurement protocol can be set up by answering six questions. Then, we discuss the benefits and pitfalls and provide recommendations for effective training of machine learning models from movement data, focusing on data pre-processing, feature calculation, and model selection and tuning. Finally, we highlight two application domains where “in the wild” data recording was combined with machine learning for injury prevention and technique analysis, respectively.

Authors with CRIS profile

Eva Dorschky Machine Learning and Data Analytics Lab Anne Koelewijn Juniorprofessur für Computational Movement Science (Stiftungsprofessur)

Additional Organisation(s)

Sonderforschungsbereich 1483/1 Empathokinästhetische Sensorik – Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (EmpkinS)

Involved external institutions

University of Twente NL Netherlands (NL) Università degli Studi di Roma "Foro Italico" IT Italy (IT) Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven BE Belgium (BE) Leopold-Franzens-Universität Innsbruck / University of Innsbruck AT Austria (AT)

How to cite

APA:

Dorschky, E., Camomilla, V., Davis, J., Federolf, P., Reenalda, J., & Koelewijn, A. (2023). Perspective on “in the wild” movement analysis using machine learning. Human Movement Science, 87. https://doi.org/10.1016/j.humov.2022.103042

MLA:

Dorschky, Eva, et al. "Perspective on “in the wild” movement analysis using machine learning." Human Movement Science 87 (2023).

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