Exercise Exertion Levels Prediction Based on Real-Time Wearable Physiological Signal Monitoring

Aref Smiley, Te Yi Tsai, Elena Zakashansky, Aileen Gabriel, Taulant Xhakli, Wanting Cui, Xingyue Huo, Ihor Havrylchuk, Hu Cui, Joseph Finkelstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The real-time revolutions per minute (RPM) data, ECG signal, pulse rate, and oxygen saturation levels were collected during 16-minute cycling exercises. In parallel, ratings of perceived exertion (RPE) were collected each minute from the study participants. A 2-minute moving window, with one minute shift, was applied to each 16-minute exercise session to divide it into a total of fifteen 2-minute windows. Based on the self-reported RPE, each exercise window was labeled as 'high exertion' or 'low exertion' classes. The heart rate variability (HRV) characteristics in time and frequency domains were extracted from the collected ECG signals for each window. In addition, collected oxygen saturation levels, pulse rate, and RPMs were averaged for each window. The best predictive features were then selected using the minimum redundancy maximum relevance (mRMR) algorithm. Top selected features were then used to assess the accuracy of five ML classifiers to predict the level of exertion. The Naïve Bayes model demonstrated the best performance with an accuracy of 80% and an F1 score of 79%.

Original languageEnglish
Title of host publicationHealthcare Transformation with Informatics and Artificial Intelligence
EditorsJohn Mantas, Parisis Gallos, Emmanouil Zoulias, Arie Hasman, Mowafa S. Househ, Martha Charalampidou, Andriana Magdalinou
PublisherIOS Press BV
Pages172-175
Number of pages4
ISBN (Electronic)9781643684000
DOIs
StatePublished - 29 Jun 2023
Externally publishedYes
Event21st International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023 - Athens, Greece
Duration: 1 Jul 20233 Jul 2023

Publication series

NameStudies in Health Technology and Informatics
Volume305
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference21st International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023
Country/TerritoryGreece
CityAthens
Period1/07/233/07/23

Keywords

  • Aerobic exercise
  • exertion level
  • heart rate variability
  • machine learning

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