Peleg Dori, Braiman Eyal, Yom-Tov Elad, Inbar Gideon F
Faculty of Electrical Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel.
IEEE Trans Neural Syst Rehabil Eng. 2002 Dec;10(4):290-3. doi: 10.1109/TNSRE.2002.806831.
Hand amputees would highly benefit from a robotic prosthesis, which would allow the movement of a number of fingers. In this paper we propose using the electromyographic signals recorded by two pairs of electrodes placed over the arm for operating such prosthesis. Multiple features from these signals are extracted whence the most relevant features are selected by a genetic algorithm as inputs for a simple classifier. This method results in a probability of error of less than 2%.
手部截肢者将从机器人假肢中受益匪浅，这种假肢可以实现多个手指的运动。在本文中，我们建议使用放置在手臂上的两对电极记录的肌电信号来操作这种假肢。从这些信号中提取多个特征，然后通过遗传算法选择最相关的特征作为简单分类器的输入。该方法的错误概率小于2%。
