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arXiv · 2406.06339

Audio-based Step-count Estimation for Running -- Windowing and Neural Network Baselines

Philipp Wagner·Andreas Triantafyllopoulos·Alexander Gebhard·Björn Schuller
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Abstract

In recent decades, running has become an increasingly popular pastime activity due to its accessibility, ease of practice, and anticipated health benefits. However, the risk of running-related injuries is substantial for runners of different experience levels. Several common forms of injuries result from overuse -- extending beyond the recommended running time and intensity. Recently, audio-based tracking has emerged as yet another modality for monitoring running behaviour and performance, with previous studies largely concentrating on predicting runner fatigue. In this work, we investigate audio-based step count estimation during outdoor running, achieving a mean absolute error of 1.098 in window-based step-count differences and a Pearson correlation coefficient of 0.479 when predicting the number of steps in a 5-second window of audio. Our work thus showcases the feasibility of audio-based monitoring for estimating important physiological variables and lays the foundations for further utilising audio sensors for a more thorough characterisation of runner behaviour.

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Philipp Wagner, Andreas Triantafyllopoulos, Alexander Gebhard, Björn Schuller. 2024-06-10. Audio-based Step-count Estimation for Running -- Windowing and Neural Network Baselines. https://doi.org/10.23919/eusipco63174.2024.10715110

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