Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors

Aref Smiley; Te Yi Tsai; Ihor Havrylchuk; Aileen Gabriel; Elena Zakashansky; Taulant Xhakli; Jinyan Lyu; Wanting Cui; Irena Parvanova; Joseph Finkelstein

doi:10.3233/SHTI231228

Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors

Aref Smiley
, Te Yi Tsai
, Ihor Havrylchuk
, Aileen Gabriel
, Elena Zakashansky
, Taulant Xhakli
, Jinyan Lyu
, Wanting Cui
, Irena Parvanova
, Joseph Finkelstein

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

This research aimed to develop a model for real-time prediction of aerobic exercise exertion levels. ECG signals were registered during 16-minute cycling exercises. Perceived ratings of exertion (RPE) were collected each minute from the study participants. Based on the reported RPE, each consecutive minute of the exercise was assigned to the 'high exertion' or 'low exertion' class. The characteristics of heart rate variability (HRV) in time and frequency domains were used as predictive features. The top ten ranked predictive features were selected using the minimum redundancy maximum relevance (mRMR) algorithm. The support vector machine demonstrated the highest accuracy with an F1 score of 82%.

Original language	English (US)
Title of host publication	MEDINFO 2023 - The Future is Accessible
Subtitle of host publication	Proceedings of the 19th World Congress on Medical and Health Informatics
Editors	Jen Bichel-Findlay, Paula Otero, Philip Scott, Elaine Huesing
Publisher	IOS Press BV
Pages	1428-1429
Number of pages	2
ISBN (Electronic)	9781643684567
DOIs	https://doi.org/10.3233/SHTI231228
State	Published - Jan 25 2024
Externally published	Yes
Event	19th World Congress on Medical and Health Informatics, MedInfo 2023 - Sydney, Australia Duration: Jul 8 2023 → Jul 12 2023

Publication series

Name	Studies in Health Technology and Informatics
Volume	310
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	19th World Congress on Medical and Health Informatics, MedInfo 2023
Country/Territory	Australia
City	Sydney
Period	7/8/23 → 7/12/23

Keywords

aerobic exercise
exertion level
Machine learning

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/SHTI231228

Cite this

Smiley, A., Tsai, T. Y., Havrylchuk, I., Gabriel, A., Zakashansky, E., Xhakli, T., Lyu, J., Cui, W., Parvanova, I., & Finkelstein, J. (2024). Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors. In J. Bichel-Findlay, P. Otero, P. Scott, & E. Huesing (Eds.), MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics (pp. 1428-1429). (Studies in Health Technology and Informatics; Vol. 310). IOS Press BV. https://doi.org/10.3233/SHTI231228

Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors. / Smiley, Aref; Tsai, Te Yi; Havrylchuk, Ihor et al.
MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics. ed. / Jen Bichel-Findlay; Paula Otero; Philip Scott; Elaine Huesing. IOS Press BV, 2024. p. 1428-1429 (Studies in Health Technology and Informatics; Vol. 310).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Smiley, A, Tsai, TY, Havrylchuk, I, Gabriel, A, Zakashansky, E, Xhakli, T, Lyu, J, Cui, W, Parvanova, I & Finkelstein, J 2024, Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors. in J Bichel-Findlay, P Otero, P Scott & E Huesing (eds), MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 310, IOS Press BV, pp. 1428-1429, 19th World Congress on Medical and Health Informatics, MedInfo 2023, Sydney, Australia, 7/8/23. https://doi.org/10.3233/SHTI231228

Smiley A, Tsai TY, Havrylchuk I, Gabriel A, Zakashansky E, Xhakli T et al. Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors. In Bichel-Findlay J, Otero P, Scott P, Huesing E, editors, MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics. IOS Press BV. 2024. p. 1428-1429. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI231228

Smiley, Aref ; Tsai, Te Yi ; Havrylchuk, Ihor et al. / Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors. MEDINFO 2023 - The Future is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics. editor / Jen Bichel-Findlay ; Paula Otero ; Philip Scott ; Elaine Huesing. IOS Press BV, 2024. pp. 1428-1429 (Studies in Health Technology and Informatics).

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Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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