Anam Khairul, Al-Jumaily Adel
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:4192-5. doi: 10.1109/EMBC.2014.6944548.
The use of a small number of surface electromyography (EMG) channels on the transradial amputee in a myoelectric controller is a big challenge. This paper proposes a pattern recognition system using an extreme learning machine (ELM) optimized by particle swarm optimization (PSO). PSO is mutated by wavelet function to avoid trapped in a local minima. The proposed system is used to classify eleven imagined finger motions on five amputees by using only two EMG channels. The optimal performance of wavelet-PSO was compared to a grid-search method and standard PSO. The experimental results show that the proposed system is the most accurate classifier among other tested classifiers. It could classify 11 finger motions with the average accuracy of about 94 % across five amputees.
在肌电控制器中，为经桡骨截肢者使用少量表面肌电图（EMG）通道是一项巨大挑战。本文提出了一种使用经粒子群优化（PSO）优化的极限学习机（ELM）的模式识别系统。通过小波函数对PSO进行变异，以避免陷入局部最小值。所提出的系统仅使用两个EMG通道对五名截肢者的十一种想象手指运动进行分类。将小波 - PSO的最佳性能与网格搜索方法和标准PSO进行了比较。实验结果表明，所提出的系统是其他测试分类器中最准确的分类器。它可以对五名截肢者的11种手指运动进行分类，平均准确率约为94％。
