Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695, United States of America.
Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States of America.
J Neural Eng. 2021 Jul 27;18(4). doi: 10.1088/1741-2552/ac1176.
Advanced robotic lower limb prostheses are mainly controlled autonomously. Although the existing control can assist cyclic movements during locomotion of amputee users, the function of these modern devices is still limited due to the lack of neuromuscular control (i.e. control based on human efferent neural signals from the central nervous system to peripheral muscles for movement production). Neuromuscular control signals can be recorded from muscles, called electromyographic (EMG) or myoelectric signals. In fact, using EMG signals for robotic lower limb prostheses control has been an emerging research topic in the field for the past decade to address novel prosthesis functionality and adaptability to different environments and task contexts. The objective of this paper is to review robotic lower limb Prosthesis control via EMG signals recorded from residual muscles in individuals with lower limb amputations.We performed a literature review on surgical techniques for enhanced EMG interfaces, EMG sensors, decoding algorithms, and control paradigms for robotic lower limb prostheses.This review highlights the promise of EMG control for enabling new functionalities in robotic lower limb prostheses, as well as the existing challenges, knowledge gaps, and opportunities on this research topic from human motor control and clinical practice perspectives.This review may guide the future collaborations among researchers in neuromechanics, neural engineering, assistive technologies, and amputee clinics in order to build and translate true bionic lower limbs to individuals with lower limb amputations for improved motor function.
高级机器人下肢假肢主要是自主控制的。虽然现有的控制方法可以辅助截肢用户在运动时进行周期性运动,但由于缺乏神经肌肉控制(即基于中枢神经系统向周围肌肉发出的传出神经信号来进行运动产生的控制),这些现代设备的功能仍然有限。神经肌肉控制信号可以从肌肉中记录下来,称为肌电图(EMG)或肌电信号。事实上,在过去十年中,使用 EMG 信号来控制机器人下肢假肢一直是该领域的一个新兴研究课题,旨在解决新型假肢的功能以及对不同环境和任务背景的适应性。本文的目的是通过记录下肢截肢者残肢肌肉的 EMG 信号来回顾机器人下肢假肢的控制。我们对增强 EMG 接口的手术技术、EMG 传感器、解码算法以及机器人下肢假肢的控制范式进行了文献回顾。本综述从人类运动控制和临床实践的角度强调了 EMG 控制在为机器人下肢假肢带来新功能方面的前景,以及在这个研究课题上存在的挑战、知识空白和机遇。本综述可能会指导神经力学、神经工程、辅助技术和截肢诊所的研究人员之间的未来合作,以便为下肢截肢者构建和转化真正的仿生下肢,从而提高运动功能。