Gait Phase Estimation of Unsupervised Outdoors Walking Using IMUs and a Linear Regression Model
Gait Phase Estimation of Unsupervised Outdoors Walking Using IMUs and a Linear Regression Model
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DOI：
10.1109/access.2022.3227344
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
A. Soliman;G. Ribeiro;Andres Torres;Li-Fan Wu;M. Rastgaar
中科院分区：
文献类型：
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作者：
A. Soliman;G. Ribeiro;Andres Torres;Li-Fan Wu;M. Rastgaar
人体步态分析和检测对于包括可穿戴和康复机器人设备在内的许多应用都是至关重要的，从而降低或跟踪受伤风险。这项拟议的工作允许研究人员在无人监督的户外环境中研究受试者的步态阶段，而不需要固定的阈值和嵌入传感器的鞋垫。我们提出了一种基于两个人体佩戴的惯性测量单元(IMU)的模式来标记步态事件的实验协议。步态模式是使用测力板和运动捕捉系统开发的。在定义步态模式后，受试者在户外步行40分钟以训练和测试基于主成分分析(PCA)的线性回归模型。接下来，使用来自其他人类受试者的定义模式来执行步态相位估计，以适应运动捕捉和力板数据不可用的情况。结果显示，所有受试者的归一化步态相位估计误差最小为1.81%，最大为2.48%，平均为2.21~0.258美元%。结果尤其重要，因为拟议的工作可以扩展到对人类辅助设备、康复设备和临床步态分析的精确控制。
Human gait analysis and detection are critical for many applications, including wearable and rehabilitation robotic devices, reducing or tracking injury risk. The proposed work allows researchers to study the gait phase of human subjects in an unsupervised outdoor environment without the need for fixed thresholds and sensor-embedded insoles. We present an experimental protocol to label gait events based on patterns in human subjects from two body-worn inertial measurement units (IMUs). Gait patterns are developed using a force plate and a motion capture system. Upon defining the gait pattern, human subjects walk outdoors for forty minutes to train and test a principal component analysis (PCA)-based linear regression model. Next, gait phase estimation is performed using the defined patterns from other human subjects to accommodate cases where motion capture and force plate data are unavailable. Results showed a minimum normalized gait phase estimation error of 1.81 %, a maximum of 2.48 %, and an average of $2.21~\pm ~0.258$ % for all subjects involved. Results are particularly significant because the proposed work can be expanded to precise control of human-assistive devices, rehabilitation devices, and clinical gait analysis.