Modeling of the Rating of Perceived Exertion Based on Heart Rate Using Machine Learning Methods.
An Acad Bras Cienc
; 95(2): e20201723, 2023.
Article
em En
| MEDLINE
| ID: mdl-37018836
Rating of perceived exertion (RPE) can serve as a more convenient and economical alternative to heart rate (HR) for exercise intensity control. This study aims to explore the influence of factors, such as indicators of demographic, anthropometric, body composition, cardiovascular function and basic exercise ability on the relationship between HR and RPE, and to develop the model predicting RPE from HR. 48 healthy participants were recruited to perform an incrementally 6-stage pedaling test. HR and RPE were collected during each stage. The influencing factors were identified with the forward selection method to train Gaussian Process regression (GPR), support vector machine (SVM) and linear regression models. Metrics of R2, adjusted R2 and RMSE were calculated to evaluate the performance of the models. The GPR model outperformed the SVM and linear regression models, and achieved an R2 of 0.95, adjusted R2 of 0.89 and RMSE of 0.52. Indicators of age, resting heart rate (RHR), Central arterial pressure (CAP), body fat rate (BFR) and body mass index (BMI) were identified as factors that best predicted the relationship between RPE and HR. It is possible to use GPR model to estimate RPE from HR accurately, after adjusting for age, RHR, CAP, BFR and BMI.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Exercício Físico
/
Esforço Físico
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
An Acad Bras Cienc
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China
País de publicação:
Brasil