Injuries to the knee joint are very common for long-distance and frequent runners, an issue which is often attributed to fatigue. We address the problem of fatigue detection from biomechanical data from different sources, consisting of lower extremity joint angles and ground reaction forces from running athletes with the goal of better understanding the impact of fatigue on the biomechanics of runners in general and on an individual level. This is done by sequentially testing for change in a datastream using a simple martingale test statistic. Time-uniform probabilistic martingale bounds are provided which are used as thresholds for the test statistic. Sharp bounds can be developed by a hybrid of a piece-wise linear- and a law of iterated logarithm- bound over all time regimes, where the probability of an early detection is controlled in a uniform way. If the underlying distribution of the data gradually changes over the course of a run, then a timely upcrossing of the martingale over these bounds is expected. The methods are developed for a setting when change sets in gradually in an incoming stream of data. Parameter selection for the bounds are based on simulations and methodological comparison is done with respect to existing advances. The algorithms presented here can be easily adapted to an online change-detection setting. Finally, we provide a detailed data analysis based on extensive measurements of several athletes and benchmark the fatigue detection results with the runners' individual feedback over the course of the data collection. Qualitative conclusions on the biomechanical profiles of the athletes can be made based on the shape of the martingale trajectories even in the absence of an upcrossing of the threshold.

翻译：膝关节损伤在长距离及高频跑步者中极为常见，该问题往往归因于疲劳。本文研究从多源生物力学数据中检测疲劳的问题，数据集包含跑步运动员的下肢关节角度和地面反作用力，旨在从群体和个体层面深入理解疲劳对跑步者生物力学特征的影响。我们采用简洁鞅检验统计量对数据流进行序贯变化检测，通过时间一致的概率鞅边界作为检验统计量的阈值。通过分段线性边界与迭代对数边界的混合策略，可在所有时间尺度下构建精确边界，并以统一方式控制早期检测概率。若数据潜在分布随跑步进程逐步变化，则鞅统计量将预期在适当时刻跨越该边界。本文方法适用于数据流中渐变特征的应用场景，基于仿真实验完成边界参数选择，并与现有进展进行了方法论对比。所提算法可便捷适配在线变化检测框架。最后，基于多名运动员的实测数据开展详尽数据分析，结合数据采集周期内运动员自我反馈对疲劳检测结果进行基准测试。即便未发生阈值跨越事件，仍可根据鞅轨迹形态对运动员生物力学特征得出定性结论。