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Modeling of the Rating of Perceived Exertion Based on Heart Rate Using Machine Learning Methods.
Zhao, Huanhuan; Xu, Yang; Wu, Yichen; Ma, Zuchang; Ding, Zenghui; Sun, Yining.
Afiliação
  • Zhao H; Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushan Lake Road, Hefei, 230031, China.
  • Xu Y; University of Science and Technology of China, 96 Jinzhai Road, Hefei, 230026, China.
  • Wu Y; School of Computer and Information Engineering, Chuzhou University, 1 Huifeng West Road, Chuzhou, 239000, China.
  • Ma Z; Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushan Lake Road, Hefei, 230031, China.
  • Ding Z; Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushan Lake Road, Hefei, 230031, China.
  • Sun Y; University of Science and Technology of China, 96 Jinzhai Road, Hefei, 230026, China.
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.
Assuntos

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

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