Gait Event Anomaly Detection and Correction During a Split-Belt Treadmill Task
Gait Event Anomaly Detection and Correction During a Split-Belt Treadmill Task
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DOI：
10.1109/access.2019.2918559
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Signal, Nada
中科院分区：
文献类型：
--
作者：
Rashid, Usman;Kumari, Nitika;Signal, Nada
在仪表分体带跑步机任务中，避免部分踩到对侧跑带是具有挑战性的。如果发生这种情况，则力传感器无法准确检测步态事件，因为力数据无效。在本文中，我们提出了一种算法，该算法使用基于加速度导数的测量自动检测这些无效力数据。我们将此算法与基于坐标的跑步机算法结合使用，将力传感器检测到的无效步态事件替换为 3D 标记检测到的步态事件。使用接收器操作员特征、曲线下面积和 Youden 指数，根据从相同速度和不同速度配置下的健康参与者收集的数据的目视检查来评估所提出的算法的性能。我们发现，在相同速度和不同速度配置下，曲线下面积 (AUC) 得分均高于 0.8。此外，没有足够的证据（p > 0.05）表明步行速度与算法性能之间存在相关性。我们得出的结论是，该算法具有良好到优异的检测和校正性能，这对于涉及使用仪器分带式跑步机进行步态分析的研究很有用。还介绍了所提出的算法的基于 MATLAB（MathWorks，Inc.，Natick，MA，USA）的实现和示例数据文件。
During instrumented split-belt treadmill tasks, it is challenging to avoid partially stepping on the contralateral belt. If this occurs, accurate detection of gait events from force sensors becomes impossible, as the force data are invalidated. In this paper, we present an algorithm, which automatically detects these invalid force data using an acceleration derivative-based measure. We used this algorithm in combination with the coordinate-based treadmill algorithm to replace the invalidated gait events detected from force sensors with those detected from 3-D markers. The performance of the proposed algorithm was evaluated against the visual examination of data collected from healthy participants in both the same speed and differential speed configurations, using the receiver operator characteristics, the area under the curve, and the Youden index. We found that the area under the curve (AUC) score was above 0.8 in both the same speed and differential speed configurations. Moreover, there was not enough evidence (p > 0.05) to suggest a correlation between walking speed and the performance of the algorithm. We conclude that the algorithm has good to excellent detection and correction performance, which can be useful for research involving analysis of gait with instrumented split-belt treadmills. A MATLAB (MathWorks, Inc., Natick, MA, USA) based implementation of the proposed algorithm and example data files are also presented.