Exercise Exertion Level Prediction Using Data from Wearable Physiologic Monitors.
AMIA Annu Symp Proc
; 2023: 653-662, 2023.
Article
em En
| MEDLINE
| ID: mdl-38222331
ABSTRACT
This study aims to develop machine learning (ML) algorithms to predict exercise exertion levels using physiological parameters collected from wearable devices. Real-time ECG, oxygen saturation, pulse rate, and revolutions per minute (RPM) data were collected at three intensity levels during a 16-minute cycling exercise. Parallel to this, throughout each exercise session, the study subjects' ratings of perceived exertion (RPE) were gathered once per minute. Each 16-minute exercise session was divided into a total of eight 2-minute windows. Each exercise window was labeled as "high exertion," or "low exertion" classes based on the self-reported RPEs. For each window, the gathered ECG data were used to derive the heart rate variability (HRV) features in the temporal and frequency domains. Additionally, each window's averaged RPMs, heart rate, and oxygen saturation levels were calculated to form all the predictive features. The minimum redundancy maximum relevance algorithm was used to choose the best predictive features. Top selected features were then used to assess the accuracy of ten ML classifiers to predict the next window's exertion level. The k-nearest neighbors (KNN) model showed the highest accuracy of 85.7% and the highest F1 score of 83%. An ensemble model showed the highest area under the curve (AUC) of 0.92. The suggested method can be used to automatically track perceived exercise exertion in real-time.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Esforço Físico
/
Dispositivos Eletrônicos Vestíveis
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
AMIA Annu Symp Proc
Ano de publicação:
2023
Tipo de documento:
Article