Bin Ahmad Nadzri Ahmad Akmal, Ahmad Siti Anom, Marhaban Mohd Hamiruce, Jaafar Haslina
Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia,
Australas Phys Eng Sci Med. 2014 Mar;37(1):133-7. doi: 10.1007/s13246-014-0243-3. Epub 2014 Jan 18.
Surface electromyography (SEMG) signals can provide important information for prosthetic hand control application. In this study, time domain (TD) features were used in extracting information from the SEMG signal in determining hand motions and stages of contraction (start, middle and end). Data were collected from ten healthy subjects. Two muscles, which are flexor carpi ulnaris (FCU) and extensor carpi radialis (ECR) were assessed during three hand motions of wrist flexion (WF), wrist extension (WE) and co-contraction (CC). The SEMG signals were first segmented into 132.5 ms windows, full wave rectified and filtered with a 6 Hz low pass Butterworth filter. Five TD features of mean absolute value, variance, root mean square, integrated absolute value and waveform length were used for feature extraction and subsequently patterns were determined. It is concluded that the TD features that were used are able to differentiate hand motions. However, for the stages of contraction determination, although there were patterns observed, it is determined that the stages could not be properly be differentiated due to the variability of signal strengths between subjects.
表面肌电图（SEMG）信号可为假肢手控制应用提供重要信息。在本研究中，时域（TD）特征被用于从SEMG信号中提取信息，以确定手部运动和收缩阶段（开始、中间和结束）。数据收集自十名健康受试者。在腕部屈曲（WF）、腕部伸展（WE）和共同收缩（CC）这三种手部运动过程中，对尺侧腕屈肌（FCU）和桡侧腕伸肌（ECR）这两块肌肉进行了评估。SEMG信号首先被分割成132.5毫秒的窗口，进行全波整流，并使用6赫兹的低通巴特沃斯滤波器进行滤波。使用均值绝对值、方差、均方根、积分绝对值和波形长度这五个TD特征进行特征提取，随后确定模式。得出的结论是，所使用的TD特征能够区分手部运动。然而，对于收缩阶段的确定，尽管观察到了模式，但由于受试者之间信号强度的变异性，确定无法正确区分这些阶段。
