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

arXiv: https://arxiv.org/abs/2406.06339 · HTML: https://mlsanity.com/paper/2406.06339/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [How Class Ontology and Data Scale Affect Audio Transfer Learning](https://mlsanity.com/paper/2603.25476/) (2026)

## Abstract (arXiv)

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.