Audio-based Step-count Estimation for Running -- Windowing and Neural Network Baselines
<|reference_start|>Audio-based Step-count Estimation for Running -- Windowing and Neural Network Baselines: 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.<|reference_end|>
@article{wagner2024audio-based,
    title={Audio-based Step-count Estimation for Running -- Windowing and Neural
  Network Baselines},
    author={Philipp Wagner, Andreas Triantafyllopoulos, Alexander Gebhard, Bj"orn
  Schuller},
    journal={arXiv preprint arXiv:2406.06339},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06339},
    primaryClass={cs.SD eess.AS}
}