基于音频的跑步步数估计——窗口化与神经网络基线
Audio-based Step-count Estimation for Running -- Windowing and Neural Network Baselines
AI总结：
本文研究户外跑步中基于音频的步数估计，采用窗口化方法和神经网络基线，在5秒音频窗口上实现步数差异平均绝对误差1.098、皮尔逊相关系数0.479，验证了音频监测生理变量的可行性。
摘要：
近几十年来，跑步因其便利性、易于练习以及预期的健康益处而成为一种日益流行的休闲活动。然而，不同经验水平的跑步者都面临相当大的跑步相关损伤风险。几种常见的损伤形式源于过度使用——超出推荐的跑步时间和强度。近年来，基于音频的追踪已成为监测跑步行为和表现的又一种模态，以往研究主要集中于预测跑步者疲劳。在本工作中，我们研究了户外跑步期间基于音频的步数估计，在基于窗口的步数差异上实现了1.098的平均绝对误差，并在预测5秒音频窗口中的步数时实现了0.479的皮尔逊相关系数。因此，我们的工作展示了基于音频监测在估计重要生理变量方面的可行性，并为进一步利用音频传感器更全面地刻画跑步者行为奠定了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
