Prediction of instantaneous perceived effort during outdoor running using accelerometry and machine learning.

ABSTRACT: The rate of perceived effort (RPE) is a subjective scale widely used for defining training loads. However, the subjective nature of the metric might lead to an inaccurate representation of the imposed metabolic/mechanical exercise demands. Therefore, this study aimed to predict the rate of perceived exertions during running using biomechanical parameters extracted from a commercially available running smartwatch. Forty-three recreational runners performed a simulated 5-km race on a track, providing their RPE from a Borg scale (6-20) every 400 m. Running distance, heart rate, foot contact time, cadence, stride length, and vertical oscillation were extracted from a running smartwatch (Garmin 735XT). Machine learning regression models were trained to predict the RPE at every 5 s of the 5-km r

SUBMITTER: Pirscoveanu CI 

PROVIDER: S-EPMC10879226 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature