利用肌肉活动和形态的外周感知连续预测行走过程中的腿部运动学

Kaitlin G. Rabe, Nicholas P. Fey
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引用次数: 2

摘要

机器人下肢辅助装置的进步提高了对用户意图的准确和连续感知的需求。表面肌电图(EMG)已广泛用于感觉肌肉,并估计运动模式和肢体运动。最近,声声图也作为一种新的感知方式进行了研究。然而,多种传感模式的融合尚未被用于连续预测下肢的多个自由度,以及在多个行走任务中。在本研究中,9名身体健全的受试者完成水平、倾斜、下降、楼梯上升和楼梯下降任务。在每个任务中收集运动捕捉数据,以及来自大腿前部的便携式超声传感器(横向排列)和下肢八块肌肉上的表面肌电传感器的数据。使用三个特征集(1)表面肌电图,(2)声纳图,(3)肌电图与声纳图的传感器融合),实现了与受试者相关、与任务无关的高斯过程回归模型,用于在这些行走任务中连续预测膝关节和踝关节的角度和角速度。令人惊讶的是,在所有任务中,声纳图和基于传感器融合的膝关节或踝关节角度和角速度预测之间没有显著差异。然而,与表面肌图相比,声纳和传感器融合使得所有行走任务中膝关节角度预测的均方根误差和大多数行走任务中膝关节角速度预测的均方根误差减小。与表面肌电图相比,传感器融合改善了除爬楼梯外所有步行任务的踝关节角度预测。总体而言,踝关节角速度预测导致了最低的性能。临床相关性:本研究比较了表面肌电图和声纳图的结合,以及每一种单独的模式,在广泛变化的运动任务中对膝关节和踝关节运动学的连续预测。
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Continuous Prediction of Leg Kinematics During Ambulation using Peripheral Sensing of Muscle Activity and Morphology
The advancement of robotic lower-limb assistive devices has heightened the need for accurate and continuous sensing of user intent. Surface electromyography (EMG) has been extensively used to sense muscles, and estimate locomotion modes and limb motion. Recently, sonomyography has also been investigated as a novel sensing modality. However, the fusion of multiple sensing modalities has not been explored for the continuous prediction of multiple degrees-of-freedom of the lower limb, and during multiple ambulation tasks. In the present study, nine able-bodied subjects completed level, incline, decline, stair ascent, and stair descent tasks. Motion capture data was collected during each task, as well as data from a portable ultrasound transducer (aligned in a transverse orientation) on the anterior thigh and surface EMG sensors on eight lower-limb muscles. Subject-dependent, task-independent Gaussian process regression models were implemented for continuous prediction of knee and ankle angle and angular velocity during these ambulation tasks using three feature sets: (1) surface EMG, (2) sonomyography, and (3) sensor fusion of EMG with sonomyography. Surprisingly, there were no significant differences between sonomyography and sensor fusion-based prediction of knee or ankle angle and angular velocity during all tasks. However, sonomyography and sensor fusion resulted in reduced root mean square error of knee angle prediction during all ambulation tasks and knee angular velocity prediction during most ambulation tasks compared to surface EMG. Sensor fusion improved ankle angle prediction for all walking tasks except stair ascent in comparison to surface EMG. Ankle angular velocity prediction resulted in the lowest performance, overall.Clinical Relevance—This work compares the combination of surface electromyography and sonomyography, and each modality in isolation, for the continuous prediction of kinematics of the knee and ankle during widely-varying ambulatory tasks.
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