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Running while fatigued poses an increased risk of injury. Wearable sensors allow the overall running kinematics or running pattern to be captured as time series signals. The changes that happen in the running pattern due to fatigue, although prominent enough to increase the risk of injury, are generally only seen as subtle differences in the signal itself and hence are difficult to differentiate using purely visual inspection. In this paper, we introduce a time series dataset of motion capture data from runners before and after a fatiguing intervention. The total dataset consists of more than 5500 instances and was collected from 19 participants. The evaluation presented in this paper first looks at the effectiveness of a data aggregation technique called time series barycenters which is shown to improve classification performance. We evaluate and compare a set of classifiers and explanation methods for this problem, and select the most informative classifier and explanation for this dataset. We then present feedback from a domain expert on the insights offered by the explanations.

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Explaining Fatigue in Runners Using Time Series Analysis on Wearable Sensor Data

  • 会议论文

  • 发表日期：

    2025年

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  • Bahavathy Kathirgamanathan,
  • Thu Trang Nguyen,
  • Brian Caulfield,
  • Georgiana Ifrim,
  • Pádraig Cunningham

摘要

Running while fatigued poses an increased risk of injury. Wearable sensors allow the overall running kinematics or running pattern to be captured as time series signals. The changes that happen in the running pattern due to fatigue, although prominent enough to increase the risk of injury, are generally only seen as subtle differences in the signal itself and hence are difficult to differentiate using purely visual inspection. In this paper, we introduce a time series dataset of motion capture data from runners before and after a fatiguing intervention. The total dataset consists of more than 5500 instances and was collected from 19 participants. The evaluation presented in this paper first looks at the effectiveness of a data aggregation technique called time series barycenters which is shown to improve classification performance. We evaluate and compare a set of classifiers and explanation methods for this problem, and select the most informative classifier and explanation for this dataset. We then present feedback from a domain expert on the insights offered by the explanations.