论文标题
使用音频数据的室外运行条件中的疲劳预测
Fatigue Prediction in Outdoor Running Conditions using Audio Data
论文作者
论文摘要
尽管跑步是一项常见的休闲活动，也是几名运动员的核心培训团，但每年$ 29 \％$ $ $ $ $ $ 79 \％的$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $％$ $ $ $。这些伤害与过度疲劳有关，这改变了某人的运行方式。在这项工作中，我们使用通过智能手机通过智能手机附上的智能环境中捕获的音频数据来探讨对Borg收到的劳累感知（RPE）量表的可行性（RPE）量表（范围：$ [6-20] $）。使用卷积神经网络（CNN）上的log-Mel频谱图，我们获得了依赖受试者的实验的平均绝对误差为$ 2.35 $，这表明音频可有效地用于建模疲劳，同时比其他传感器的信号更容易且非侵入性地获取。
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.
