Decoding gestures from the upper limb using noninvasive surface electromyogram (sEMG) signals is of keen interest for the rehabilitation of amputees, artificial supernumerary limb augmentation, gestural control of computers, and virtual/augmented realities. We show that sEMG signals recorded across an array of sensor electrodes in multiple spatial locations around the forearm evince a rich geometric pattern of global motor unit (MU) activity that can be leveraged to distinguish different hand gestures. We demonstrate a simple technique to analyze spatial patterns of muscle MU activity within a temporal window and show that distinct gestures can be classified in both supervised and unsupervised manners. Specifically, we construct symmetric positive definite (SPD) covariance matrices to represent the spatial distribution of MU activity in a time window of interest, calculated as pairwise covariance of electrical signals measured across different electrodes. This allows us to understand and manipulate multivariate sEMG timeseries on a more natural subspace -the Riemannian manifold. Furthermore, it directly addresses signal variability across individuals and sessions, which remains a major challenge in the field. sEMG signals measured at a single electrode lack contextual information such as how various anatomical and physiological factors influence the signals and how their combined effect alters the evident interaction among neighboring muscles. As we show here, analyzing spatial patterns using covariance matrices on Riemannian manifolds allows us to robustly model complex interactions across spatially distributed MUs and provides a flexible and transparent framework to quantify differences in sEMG signals across individuals. The proposed method is novel in the study of sEMG signals and its performance exceeds the current benchmarks while maintaining exceptional computational efficiency.

翻译：利用非侵入式表面肌电信号解码上肢手势，在截肢者康复、人工多肢增强、计算机手势控制以及虚拟/增强现实等领域具有重要研究意义。研究表明，通过布置在前臂多个空间位置的传感器阵列记录的肌电信号，展现出丰富的全局运动单元活动几何模式，可用于区分不同手势。本文提出一种简单技术，用于分析时间窗口内肌肉运动单元活动的空间模式，并证明不同手势可通过监督与非监督两种方式实现分类。具体而言，我们构建对称正定协方差矩阵来表示感兴趣时间窗口内运动单元活动的空间分布，该矩阵通过计算不同电极间电信号的成对协方差获得。这使得我们能够在更自然的子空间——黎曼流形上理解和处理多变量肌电时间序列，并直接解决个体间及跨实验次数的信号变异性这一领域核心难题。单个电极测量的肌电信号缺乏上下文信息，例如解剖与生理因素如何影响信号，以及这些因素的联合效应如何改变相邻肌肉间的显著交互作用。正如本文所示，利用黎曼流形上的协方差矩阵分析空间模式，可稳健地建模空间分布运动单元间的复杂交互，并为量化个体间肌电信号差异提供灵活透明的框架。该方法在肌电信号分析中具有创新性，其性能超越现有基准模型，同时保持极高的计算效率。