Mobile Robot Assisted Gait Monitoring and Dynamic Margin of Stability Estimation
Mobile Robot Assisted Gait Monitoring and Dynamic Margin of Stability Estimation
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DOI：
10.1109/tmrb.2022.3162148
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发表时间：
2022-05
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影响因子：
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通讯作者：
Zhuo Chen;Huanghe Zhang;A. Zaferiou;D. Zanotto;Yi Guo
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作者：
Zhuo Chen;Huanghe Zhang;A. Zaferiou;D. Zanotto;Yi Guo
为了评估平衡控制和跌倒的风险，在步行任务期间连续监测动态稳定性是可取的。动态稳定裕度(MoS)被广泛认为是衡量人体行走稳定性和步态平衡策略的量化指标。我们提出了一种移动机器人辅助步态监测系统，该系统能够在地面行走时领先于人类受试者。将机器人上的RGB-D Kinect传感器的实时数据与压力传感器和惯性测量单元的测量融合在一起，提出了基于卡尔曼滤波的方法来实时估计运动姿态和时空步态参数。对10个受试者的实验结果与金标准运动捕捉系统的结果进行了比较。结果表明，该方法获得了较好的MOS估计精度和较高的时空步态参数估计精度。尽管现有的MoS评估工作使用的是只能提供离线分析的可穿戴传感器，但我们提出的系统提供了实时步态监测和MoS估计，可能会评估在实验室外条件下行走时的跌倒风险。
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.