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声肌电协同上肢功能肌肉连接。

Synergistic Upper-Limb Functional Muscle Connectivity Using Acoustic Mechanomyography.

出版信息

IEEE Trans Biomed Eng. 2022 Aug;69(8):2569-2580. doi: 10.1109/TBME.2022.3150422. Epub 2022 Jul 18.

Abstract

Functional muscle network is a critical concept in describing functional synergistic muscle synchronization and functional connectivity needed for the execution of complex motor tasks. Muscle network is typically derived from decomposition of intermuscular coherence (IMC) at different frequency bands of multichannel electromyography (EMG) measurements, which potentially limits out-of-clinic applications. In this investigation, we introduce muscle network analysis to assess the functional coordination and functional connectivity of muscles based on mechanomyography (MMG). We focus on a targeted group of muscles vital for activities of daily living (ADLs) in the upper-limb. Functional muscle networks are evaluated for ten able-bodied participants and three upper-limb amputees. Muscle activity was acquired from a custom-made wearable armband of MMG sensors placed over four superficial muscles around the forearm (flexor carpi radialis (FCR), brachioradialis (BR), extensor digitorum communis (EDC), and flexor carpi ulnaris (FCU)) while participants performed four different hand gestures. Muscle connectivity analysis at multiple frequency bands shows significant topographical differences across gestures for low (i.e., 5 Hz) and high (i.e., 12 Hz) activation frequencies as well as observable network differences between amputee and non-amputee subjects. Results demonstrate MMG can be used for the analysis of functional muscle connectivity and mapping of synergistic functional synchronization of upper-limb muscles in complex movement tasks. The new physiological modality provides key insights into neural circuitry of motor coordination. Findings further offer the concomitant outcomes of demonstrating feasibility of MMG to map muscle coherence from a neurophysiological perspective and providing a mechanistic basis for its translation in human-robot interface.

摘要

功能肌肉网络是描述执行复杂运动任务所需的功能协同肌肉同步和功能连接的关键概念。肌肉网络通常是从多通道肌电图(EMG)测量的不同频带的肌肉内相干性(IMC)分解中得出的,这可能限制了临床外的应用。在这项研究中,我们引入肌肉网络分析来评估基于肌振图(MMG)的肌肉的功能协调性和功能连接。我们重点关注上肢日常生活活动(ADL)中至关重要的一组肌肉。对 10 名健康参与者和 3 名上肢截肢者进行了功能肌肉网络评估。肌肉活动是从放置在前臂周围的四个浅层肌肉(桡侧腕屈肌(FCR)、肱桡肌(BR)、指伸肌(EDC)和尺侧腕屈肌(FCU))上的定制可穿戴 MMG 传感器臂带上采集的,而参与者则执行了四个不同的手势。在多个频带的肌肉连接分析中,对于低(即 5 Hz)和高(即 12 Hz)激活频率的不同手势,以及截肢者和非截肢者之间的可观察到的网络差异,显示出显著的拓扑差异。结果表明,MMG 可用于分析功能肌肉连接,并映射复杂运动任务中上肢肌肉的协同功能同步。新的生理模态为运动协调的神经电路提供了关键见解。研究结果进一步提供了同时证明 MMG 从神经生理学角度映射肌肉相干性的可行性,并为其在人机接口中的转化提供了机械基础的结果。

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