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评估 IMU 传感器位置和行走任务对步态事件检测算法准确性的影响。

Evaluating the Impact of IMU Sensor Location and Walking Task on Accuracy of Gait Event Detection Algorithms.

机构信息

US Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Labs, Silver Spring, MD 20993, USA.

出版信息

Sensors (Basel). 2021 Jun 9;21(12):3989. doi: 10.3390/s21123989.

Abstract

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.

摘要

有几种算法利用来自 IMU 传感器的 3D 加速度和/或旋转速度向量来识别步态事件(即脚趾离地和脚跟触地)。然而,对于传感器位置和行走任务类型如何影响步态事件检测算法的准确性,人们的理解还不够清晰。为了解决这一知识空白,招募了 7 名参与者(4 男/3 女;26.0±4.0 岁),让他们在完成直走任务和障碍物导航任务的同时,在脚部和小腿上放置 IMU 来收集数据。将五种常用的算法应用于每个参与者的特定传感器位置,以识别脚趾离地和脚跟触地的步态事件。使用 IMU 和参考压力感应步道为每个传感器/算法组合计算步态指标。结果表明,使用内侧-外侧旋转速度和前-后加速度的算法对于不同的传感器位置和行走任务具有相当的鲁棒性。某些应用于脚跟和小腿外侧传感器位置的算法在计算弯曲行走的步态指标时,与直跑地面行走相比,算法性能会下降。了解某些类型的算法在特定传感器位置和任务下的表现,可以为使用可穿戴技术在实验室和真实环境中对步态进行特征描述的稳健临床方案制定提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ddf/8227677/f6889cea6067/sensors-21-03989-g001.jpg

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