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

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 race using subject-independent (leave-one-out), as well as a subject-dependent regression method. The subject-dependent method was tested using 5%, 10%, or 20% of the runner's data in the training set while using the remaining data for testing. The average root-mean-square error (RMSE) in predicting the RPE using the subject-independent method was 1.8 +/- 0.8 RPE points (range 0.6-4.1; relative RMSE similar to 12 +/- 6%) across the entire 5-km race. However, the error from subject-dependent models was reduced to 1.00 +/- 0.31, 0.66 +/- 0.20 and 0.45 +/- 0.13 RPE points when using 5%, 10%, and 20% of data for training, respectively (average relative RMSE < 7%). All types of predictions underestimated the maximal RPE in similar to 1 RPE point. These results suggest that the data accessible from commercial smartwatches can be used to predict perceived exertion, opening new venues to improve training workload monitoring.

Automated summary

This study used biomechanical parameters extracted from a commercial running smartwatch to predict the rate of perceived exertions during running. The results show that using subject-dependent regression models can accurately predict RPE, opening new possibilities for improving training workload monitoring.