Fatigue Prediction in Outdoor Running Conditions using Audio Data

TL;DR

This study demonstrates that audio data captured during outdoor running can be used with CNNs to predict perceived exertion levels, offering a non-invasive method for fatigue monitoring.

Contribution

The paper introduces a novel approach to model perceived exertion using audio data and CNNs, showing promising results in outdoor running conditions.

Findings

Mean absolute error of 2.35 in RPE prediction

Audio data effectively models fatigue levels

Non-invasive fatigue monitoring method

Abstract

Although running is a common leisure activity and a core training regiment for several athletes, between $29\%$ and $79\%$ of runners sustain an overuse injury each year. These injuries are linked to excessive fatigue, which alters how someone runs. In this work, we explore the feasibility of modelling the Borg received perception of exertion (RPE) scale (range: $[6-20]$), a well-validated subjective measure of fatigue, using audio data captured in realistic outdoor environments via smartphones attached to the runners' arms. Using convolutional neural networks (CNNs) on log-Mel spectrograms, we obtain a mean absolute error of $2.35$ in subject-dependent experiments, demonstrating that audio can be effectively used to model fatigue, while being more easily and non-invasively acquired than by signals from other sensors.