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

Author:

Smiley Aref1ORCID,Tsai Te-Yi1,Havrylchuk Ihor1,Gabriel Aileen1,Zakashansky Elena1,Xhakli Taulant1,Lyu Jinyan1,Cui Wanting1,Parvanova Irena1,Finkelstein Joseph1

Affiliation:

1. Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, USA

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%.

Publisher

IOS Press

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