A Systematic Method for Outlier Detection in Human Gait Data
A Systematic Method for Outlier Detection in Human Gait Data
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DOI：
10.1109/icorr55369.2022.9896411
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Bradley Hobbs;P. Artemiadis
中科院分区：
文献类型：
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作者：
Bradley Hobbs;P. Artemiadis
当涉及到观察和测量人类步态数据以进行进一步分析时，确定所观察到的行为是否在正常的可变性范围内，或者应该被认为是异常的，是非常具有挑战性的。此外，通常步态数据是多变量的，包括运动捕获、肌电图、力测量等，每种来源都有其独特的不规则和异常原因。本文介绍了一种独特的算法，离群检测周期性步态数据使用多个来源和多个程序，以提高整体精度。所提出的算法的性能进行评估，使用现实的合成步态数据，以衡量其准确性，一个真正客观的已知的解决方案。结果表明，所提出的方法是能够检测到91.2%的真实离群值在一个广泛的合成数据集，而只产生假阳性率为0.1%，优于其他程序通常用于步态数据离群值检测。所提出的方法是一种系统的方法，从步态数据中删除离群值，直接应用于人体生物力学，康复和机器人，并可以应用于其他科学领域处理周期性数据。
When it comes to observing and measuring human gait data for further analysis, determining whether the observed behavior is within the normal range of variability, or should be considered abnormal, is very challenging. Moreover, usually gait data are multivariate including motion capture, electromyography, force measurements, etc., each source having its own unique causes of irregularities and anomalies. This paper introduces a unique algorithm for outlier detection in periodic gait data using multiple sources and multiple procedures to improve the overall accuracy. The proposed algorithm’s performance is evaluated using realistic synthetic gait data to gauge its accuracy to a truly objective known solution. It is shown that the proposed method is able to detect 91.2% of the true outliers in an extensive synthetic dataset, while only producing false positives at a rate of 0.1%, outperforming other procedures usually utilized in gait data outlier detection. The proposed method is a systematic way of removing outliers from gait data, with direct applications to human biomechanics, rehabilitation and robotics, and can be applied to other scientific fields dealing with periodic data.