Fatigue Prediction in Outdoor Running Conditions using Audio Data

• URL: http://arxiv.org/abs/2205.04343v1
• Date: Mon, 9 May 2022 14:44:05 GMT
• Title: Fatigue Prediction in Outdoor Running Conditions using Audio Data
• Authors: Andreas Triantafyllopoulos, Sandra Ottl, Alexander Gebhard, Esther Rituerto-Gonz\'alez, Mirko Jaumann, Steffen H\"uttner, Valerie Dieter, Patrick Schneewei{\ss}, Inga Krau{\ss}, Maurice Gerczuk, Shahin Amiriparian, and Bj\"orn W. Schuller
• Abstract summary: 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 convolutional neural networks (CNNs) on log-Mel spectrograms, we obtain a mean absolute error of $2.35$ in subject-dependent experiments.
• Score: 48.43471521490844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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