Department of Mechanical Engineering, Imperial College London, London, United Kingdom.
Department of Mechanical and Aerospace Engineering, Department of Electrical and Computer Engineering, New York University, New York, NY, United States of America.
J Neural Eng. 2023 Nov 17;20(6). doi: 10.1088/1741-2552/ad017c.
. Muscle network modeling maps synergistic control during complex motor tasks. Intermuscular coherence (IMC) is key to isolate synchronization underlying coupling in such neuromuscular control. Model inputs, however, rely on electromyography, which can limit the depth of muscle and spatial information acquisition across muscle fibers.. We introduce three-dimensional (3D) muscle networks based on vibrational mechanomyography (vMMG) and IMC analysis to evaluate the functional co-modulation of muscles across frequency bands in concert with the longitudinal, lateral, and transverse directions of muscle fibers. vMMG is collected from twenty subjects using a bespoke armband of accelerometers while participants perform four hand gestures. IMC from four superficial muscles (flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris) is decomposed using matrix factorization into three frequency bands. We further evaluate the practical utility of the proposed technique by analyzing the network responses to various sensor-skin contact force levels, studying changes in quality, and discriminative power of vMMG.. Results show distinct topological differences, with coherent coupling as high as 57% between specific muscle pairs, depending on the frequency band, gesture, and direction. No statistical decrease in signal strength was observed with higher contact force.. Results support the usability vMMG as a tool for muscle connectivity analyses and demonstrate the use of IMC as a new feature space for hand gesture classification. Comparison of spectrotemporal and muscle network properties between levels of force support the robustness of vMMG-based network models to variations in tissue compression. We argue 3D models of vMMG-based muscle networks provide a new foundation for studying synergistic muscle activation, particularly in out-of-clinic scenarios where electrical recording is impractical.
肌肉网络建模映射了复杂运动任务中的协同控制。肌肉间相干性（IMC）是分离神经肌肉控制中耦合基础同步的关键。然而，模型输入依赖于肌电图，这可能限制了跨肌肉纤维获取肌肉和空间信息的深度。我们引入了基于振动肌动描记术（vMMG）和 IMC 分析的三维（3D）肌肉网络，以评估肌肉在不同频率带中的功能协同调制，同时考虑肌肉纤维的纵向、横向和横向方向。vMMG 是通过二十名参与者使用定制的加速度计臂带采集的，参与者执行四个手势。从四个浅层肌肉（桡侧腕屈肌、肱桡肌、指伸肌和尺侧腕屈肌）中分解出 IMC，使用矩阵分解将其分解为三个频带。我们进一步通过分析网络对各种传感器-皮肤接触力水平的响应，研究 vMMG 的质量和判别力的变化，来评估所提出技术的实际效用。结果显示出明显的拓扑差异，特定肌肉对之间的相干耦合高达 57%，具体取决于频带、手势和方向。随着接触力的增加，信号强度没有明显下降。结果支持 vMMG 作为肌肉连通性分析工具的可用性，并证明了 IMC 作为手势分类的新特征空间的使用。对力水平的谱时和肌肉网络特性的比较支持基于 vMMG 的网络模型对组织压缩变化的稳健性。我们认为基于 vMMG 的肌肉网络的 3D 模型为研究协同肌肉激活提供了新的基础，特别是在电记录不切实际的诊所外场景中。
