TY - JOUR
T1 - Mobile Robot Assisted Gait Monitoring and Dynamic Margin of Stability Estimation
AU - Chen, Zhuo
AU - Zhang, Huanghe
AU - Zaferiou, Antonia
AU - Zanotto, Damiano
AU - Guo, Yi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - 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.
AB - 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.
KW - Assistive robots
KW - Dynamic balance
KW - Gait
KW - Margin of stability
KW - Wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=85130772769&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85130772769&partnerID=8YFLogxK
U2 - 10.1109/TMRB.2022.3162148
DO - 10.1109/TMRB.2022.3162148
M3 - Article
AN - SCOPUS:85130772769
VL - 4
SP - 460
EP - 471
JO - IEEE Transactions on Medical Robotics and Bionics
JF - IEEE Transactions on Medical Robotics and Bionics
IS - 2
ER -