There are several algorithms that use the 3D acceleration and/or rotational velocity vectors from IMU sensors to identify gait events (i.e., toe-off and heel-strike). However, a clear understanding of how sensor location and the type of walking task effect the accuracy of gait event detection algorithms is lacking. To address this knowledge gap, seven participants were recruited (4M/3F; 26.0 ± 4.0 y/o) to complete a straight walking task and obstacle navigation task while data were collected from IMUs placed on the foot and shin. Five different commonly used algorithms to identify the toe-off and heel-strike gait events were applied to each sensor location on a given participant. Gait metrics were calculated for each sensor/algorithm combination using IMUs and a reference pressure sensing walkway. Results show algorithms using medial-lateral rotational velocity and anterior-posterior acceleration are fairly robust against different sensor locations and walking tasks. Certain algorithms applied to heel and lower lateral shank sensor locations will result in degraded algorithm performance when calculating gait metrics for curved walking compared to straight overground walking. Understanding how certain types of algorithms perform for given sensor locations and tasks can inform robust clinical protocol development using wearable technology to characterize gait in both laboratory and real-world settings.
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机译:有几种算法使用IMU传感器的3D加速度和/或旋转速度向量来识别步态事件(即,脚趾和脚跟)。然而,清楚地了解传感器位置和行走任务效果的类型如何缺乏步态事件检测算法的准确性。为解决这一知识缺口,招募了七个参与者(4M / 3F; 26.0±4.0 y / O),以完成直接行走任务和障碍导航任务,而从放置在脚和胫骨上的IMU收集数据。五种不同常用的算法来识别给定参与者的每个传感器位置都将识别托管和脚跟步态事件应用于给定参与者的每个传感器位置。使用IMU和参考压力传感走道计算每个传感器/算法组合的步态度量。结果显示使用内侧横向旋转速度和前后加速度的算法对不同的传感器位置和行走任务相当鲁棒。应用于鞋跟和下侧柄传感器位置的某些算法将在计算与直接走路相比,计算用于弯曲行走的步态度量时的降级算法性能。了解某些类型的算法对于给定的传感器位置和任务的表现可以使用可穿戴技术提供鲁棒的临床协议开发,以在实验室和现实世界中表征步态。
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