Yağmur Günay Sezen, Quivira Fernando, Erdoğmuş Deniz
Cognitive Systems Lab, Northeastern University.
Int Conf Pervasive Technol Relat Assist Environ. 2017 Jun;2017:335-338. doi: 10.1145/3056540.3076208.
The main goal of this study is analyzing whether muscle synergies based on surface electromyography (EMG) measurements could be used for hand posture classification in the context of robotic prosthetic control. Target grasps were selected according to usefulness in daily activities. Additionally, due to the feasibility constraints of robotic prosthetics, only 14 gestures (13 feasible grasps and 1 resting state) were analyzed. EMG signals of intact-limb subjects were decomposed into base and activation components for muscle activity evaluation. The results demonstrate that features based on muscle synergies derived from non-negative matrix factorization (NMF) outperform the ones derived from principal component analysis (PCA). Moreover, we also examine the robustness of these methods in the absence of electrodes (muscle importance) and show that NMF is able to provide sufficiently accurate results.
本研究的主要目标是分析基于表面肌电图（EMG）测量的肌肉协同作用是否可用于机器人假肢控制背景下的手部姿势分类。根据日常活动中的实用性选择目标抓握动作。此外，由于机器人假肢的可行性限制，仅分析了14种手势（13种可行抓握动作和1种休息状态）。将健全肢体受试者的肌电信号分解为基础和激活成分以评估肌肉活动。结果表明，基于非负矩阵分解（NMF）得出的肌肉协同作用特征优于基于主成分分析（PCA）得出的特征。此外，我们还研究了这些方法在无电极情况下（肌肉重要性）的稳健性，并表明NMF能够提供足够准确的结果。
