The Relation Between Hemiparetic Gait Patterns and Walking Function After Stroke, as Measured with Wearable Sensors.

IF 3 2区 医学 Q3 ENGINEERING, BIOMEDICAL
Brice Thomas Cleland, Madeline Kim, Sangeetha Madhavan
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Abstract

Purpose: After stroke, walking is characterized by hemiparetic patterns, quantified with force sensitive walkways and motion capture systems. Some joint-level kinematic patterns of walking also can be obtained with wearable sensors. The purpose of this project was to measure joint-level kinematic patterns during walking with wearable sensors and determine the association with walking speed and endurance in individuals with chronic stroke.

Methods: In this cross-sectional observational study, participants donned APDM Opal wearable sensors during walking tests (10-meter walk test or 6-min walk test). We extracted joint-level kinematic variables of elevation at midswing, circumduction, foot strike angle, and toe-off angle. Associations of each variable with walking speed and endurance were tested, and significantly associated variables were entered into a regression model.

Results: 68 individuals with chronic stroke were included. We found that the less affected foot strike angle, less affected toe-off angle, and more affected toe-off angle were significant predictors of walking speed (R2 ≥ 0.71, p < 0.001). Less affected toe-off angle, more affected foot strike angle, and more affected toe-off angle were significant predictors of walking endurance (R2 ≥ 0.67, p < 0.001).

Conclusion: We found consistent evidence that greater toe-off angle (may reflect greater push-off) and lesser foot strike angle (may reflect lesser foot drop) were important predictors of greater walking speed and endurance. Our results suggest that wearable sensors can provide important information about joint-level kinematic patterns that are important for walking function. This information could help therapists target interventions toward specific deficits or compensatory patterns to improve walking.

用可穿戴传感器测量中风后偏瘫步态模式与行走功能的关系。
目的:中风后,行走的特点是偏瘫模式,量化与力敏感步行和运动捕捉系统。可穿戴传感器还可以获得一些关节级的步行运动模式。该项目的目的是用可穿戴传感器测量步行时关节水平的运动学模式,并确定慢性中风患者步行速度和耐力之间的关系。方法:在这项横断面观察研究中,参与者在步行测试(10米步行测试或6分钟步行测试)中佩戴APDM Opal可穿戴传感器。我们提取了关节水平的运动变量,包括中摆、绕行、足击角和脱趾角。检验各变量与步行速度和耐力的相关性,并将显著相关变量输入回归模型。结果:共纳入68例慢性脑卒中患者。结果发现,较小的脚着地角、较小的脚着地角和较大的脚着地角是行走速度的显著预测因子(R2≥0.71,p≥0.67,p)。结论:我们发现一致的证据表明,较大的脚着地角(可能反映较大的蹬蹬)和较小的脚着地角(可能反映较小的脚着地)是行走速度和耐力的重要预测因子。我们的研究结果表明,可穿戴传感器可以提供关节水平运动模式的重要信息,这对步行功能很重要。这些信息可以帮助治疗师针对特定缺陷或代偿模式进行干预,以改善步行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annals of Biomedical Engineering
Annals of Biomedical Engineering 工程技术-工程:生物医学
CiteScore
7.50
自引率
15.80%
发文量
212
审稿时长
3 months
期刊介绍: Annals of Biomedical Engineering is an official journal of the Biomedical Engineering Society, publishing original articles in the major fields of bioengineering and biomedical engineering. The Annals is an interdisciplinary and international journal with the aim to highlight integrated approaches to the solutions of biological and biomedical problems.
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