Khezri Mahdi, Jahed Mehran
Department of Electrical Engineering, Biomedical Engineering Group, Sharif University of Technology, Tehran, Iran.
Comput Biol Med. 2009 May;39(5):433-42. doi: 10.1016/j.compbiomed.2009.02.001. Epub 2009 Apr 1.
Electromyogram signal (EMG) is an electrical manifestation of contractions of muscles. Surface EMG (sEMG) signal collected from the surface of skin has been used in diverse applications. One of its usages is in pattern recognition of hand prosthesis movements. The ability of current prosthesis devices has been generally limited to simple opening and closing tasks, minimizing their efficacy compared to natural hand capabilities. In order to extend the abilities and accuracy of prosthesis arm movements and performance, a novel sEMG pattern recognizing system is proposed. To extract more pertinent information we extracted sEMGs for selected hand movements. These features constitute our main knowledge of the signal for different hand movements. In this study, we investigated time domain, time-frequency domain and combination of these as a compound representation of sEMG signal's features to access required signal information. In order to implement pattern recognition of sEMG signals for various hand movements, two intelligent classifiers, namely artificial neural network (ANN) and fuzzy inference system (FIS), were utilized. The results indicate that our approach of using compound features with principle component analysis (PCA) as dimensionality reduction technique, and FIS as the classifier, provides the best performance for sEMG pattern recognition system.
肌电图信号（EMG）是肌肉收缩的电表现形式。从皮肤表面采集的表面肌电图（sEMG）信号已被用于多种应用中。其用途之一是用于手部假肢运动的模式识别。当前假肢装置的能力通常仅限于简单的张开和闭合任务，与自然手部能力相比，其功效较低。为了扩展假肢手臂运动的能力、准确性和性能，提出了一种新型的sEMG模式识别系统。为了提取更相关的信息，我们针对选定的手部运动提取了sEMG信号。这些特征构成了我们对不同手部运动信号的主要认识。在本研究中，我们研究了时域、时频域以及将它们组合作为sEMG信号特征的复合表示，以获取所需的信号信息。为了实现针对各种手部运动的sEMG信号模式识别，使用了两种智能分类器，即人工神经网络（ANN）和模糊推理系统（FIS）。结果表明，我们采用主成分分析（PCA）作为降维技术、FIS作为分类器来使用复合特征的方法，为sEMG模式识别系统提供了最佳性能。
