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.

翻译：虽然跑步是一项常见的休闲活动和一些运动员的核心训练团,在29美元到79美元之间,跑步者每年遭受过度使用伤害,这些受伤与过度疲劳有关,这改变了某人的运行方式。在这项工作中,我们探索模拟博格人对施压(RPE)规模(范围为:$6-20)的感知(RPE)是否可行,这是一种相当有效的主观疲劳度度度度量法,它使用通过附在跑步者手臂上的智能手机在现实户外环境中收集的音频数据。我们利用日志-Mel光谱仪上的神经神经网络(CNNs),在依赖对象的实验中,我们获得了2.35美元的绝对平均误差,表明声音可以有效地用于模拟疲劳,而比其他传感器的信号更容易和不侵入。