Using Accelerometers to Detect Activity Type in a Sport Setting: Challenges with Using Multiple Types of Conventional Machine Learning Approaches

Autor/inn/en	Pfeiffer, Karin A.; Lisee, Caroline; Westgate, Bradford S.; Kalfsbeek, Cheyenne; Kuenze, Christopher; Bell, David; Cadmus-Bertram, Lisa; Montoye, Alexander H.K.
Titel	Using Accelerometers to Detect Activity Type in a Sport Setting: Challenges with Using Multiple Types of Conventional Machine Learning Approaches
Quelle	In: Measurement in Physical Education and Exercise Science, 27 (2023) 1, S.60-72 (13 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kuenze, Christopher)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1091-367X
DOI	10.1080/1091367X.2022.2069467
Schlagwörter	Athletics; Physical Activity Level; Barriers; Measurement Equipment; Young Adults; Artificial Intelligence; Prediction; Models; College Students; Measurement Techniques; Classification; Michigan + Suchen Sie Ihr Suchwort? Leichtathletik; Messinstrument; Young adult; Junger Erwachsener; Künstliche Intelligenz; Vorhersage; Analogiemodell; Collegestudent; Messtechnik; Classification system; Klassifikation; Klassifikationssystem
Abstract	A universal approach to characterizing sport-related physical activity (PA) types in sport settings does not yet exist. Young adults (n = 30), 19-33 years, engaged in a 15-min activity session, performing warm-ups, 3-on-3 soccer, and 3-on-3 basketball. Videos were recorded and manually coded as criterion PA types (walking, running, jumping, rapid lateral movements). Participants wore an accelerometer on their right hip. Multiple machine learning models were developed and compared for predicting PA type. Most models underestimated time spent completing the activities performed least commonly. Point estimates for percent agreement, sensitivity, specificity, F-scores, and kappa were similar across models, with Hidden Markov Models (HMMs) being best at classifying rare events. Models detected activity type during sport-related movements with modest accuracy (kappas [less than or equal to] 0.40). Given the better performance of HMMs, incorporating the temporal nature of sport-related activities is important for improving sport-related PA classification. (As Provided).
Anmerkungen	Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01