期刊
IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS
卷 4, 期 2, 页码 460-471出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMRB.2022.3162148
关键词
Assistive robots; wearable sensors; gait; dynamic balance; margin of stability
资金
- U.S. National Science Foundation [IIS-1838799]
This paper proposes a mobile robot-assisted gait monitoring system that can estimate dynamic stability and spatiotemporal gait parameters in real time. Experimental results show that the proposed method achieves acceptable accuracy for the estimation of Margin of Stability (MoS) and high accuracy for spatiotemporal gait parameters. Compared to existing MoS assessment methods, this system provides real-time assessment of fall risk during walking in out-of-lab conditions.
To assess balance control and fall risk, it is desirable to continuously monitor dynamic stability during walking tasks. Dynamic Margin of Stability (MoS) is widely recognized as a quantitative measure for human walking stability and gait balance strategies. We propose a mobile robot assisted gait monitoring system that precedes human subjects in overground walking. Real-time data from the RGB-D Kinect sensor on the robot are fused with measurement from pressure sensors and inertial measurement units in a pair of instrumented footwear, and Kalman filter based methods are developed to estimate MoS and spatiotemporal gait parameters in real time. Experimental results with 10 subjects are compared with those obtained by a gold-standard motion capture system. Results show that the proposed method achieves acceptable accuracy of MoS estimation and high accuracy for spatio-temporal gait parameters. Whereas existing works on MoS assessment use wearable sensors that can only provide offline analysis, our proposed system provides real time gait monitoring and MoS estimation that could potentially assess fall risk during walking in out-of-lab conditions.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据