IEEE Trans Biomed Circuits Syst. 2010 Apr;4(2):101-11. doi: 10.1109/TBCAS.2009.2037604.
Surface electromyogram (SEMG) is a common method of measurement of muscle activity. It is noninvasive and is measured with minimal risk to the subject. The analysis of SEMG signal depends on a number of factors, such as amplitude as well as time- and frequency-domain properties. In the present investigation, the study of SEMG signals at different below elbow muscles for four operations of the hand wrist/grip-like opening (op)/closing (cl)/down (d)/up (u) was carried out. Myoelectric signals were extracted by using a single-channel SEMG amplifier consisting of a differential amplifier, noninverting amplifier, and interface module. Matlab softscope was used to acquire the SEMG signal from the hardware. After acquiring the data from six selected locations, interpretations were made for the estimation of parameters of the SEMG using the Matlab-filter algorithm and the fast Fourier transform technique. An interpretation of wrist/grip operations using principal component analysis (PCA) was carried out. PCA was used to identify the best SEMG signal capturing system out of two-channel, three-channel, and four-channel systems. Two acupressure points (on wrist) were also selected for the analysis with other points on the arm. SEMG signal's study at different locations, including pressure points, will be a very helpful tool for the researchers in understanding the behavior of SEMG for the development of the prosthetic hand.
表面肌电图（SEMG）是一种常用的肌肉活动测量方法。它是非侵入性的，对受试者的风险极小。SEMG 信号的分析取决于许多因素，如幅度以及时频域特性。在本研究中，对四个手手腕/抓握样张开（op）/闭合（cl）/向下（d）/向上（u）操作的不同肘下肌肉的 SEMG 信号进行了研究。肌电信号通过由差分放大器、非反相放大器和接口模块组成的单通道 SEMG 放大器提取。使用 Matlab softscope 从硬件获取 SEMG 信号。从六个选定的位置获取数据后，使用 Matlab 滤波器算法和快速傅里叶变换技术对 SEMG 的参数进行估计。使用主成分分析（PCA）对手腕/抓握操作进行了解释。PCA 用于从双通道、三通道和四通道系统中识别出最佳的 SEMG 信号采集系统。还选择了两个穴位（手腕上）与手臂上的其他穴位一起进行分析。对不同位置（包括穴位）的 SEMG 信号的研究，将是研究人员理解 SEMG 行为以开发假肢的非常有用的工具。
