Title :
A Study on Multi-motion Pattern Recognition of EMG Based on Genetic Algorithm
Author :
Zhang Qingju ; Shi Kai
Author_Institution :
Dept. of Inf. Eng., Shandong Univ. of Sci. & Technol., Taian, China
Abstract :
The traditional neural network has the uncertain shortcomings on structures and the genetic algorithm has the optimization ability. So the improved adaptive genetic algorithm is used to optimize the numbers and value of hidden nodes of neural network. And then LMS algorithm is used to optimize the weight and threshold value of the neural network. The nueral network is trainned by much groups of training samples and the ultimate neural network system is got. Afterwards, using the improved power spectrum K value method to extract the characteristic value of the collected surface emg signals;Finally, the characteristic values of six kinds of hand motions is input into RBF neural network classifier for pattern recognition. This trial gets good results and 6 kinds of action recognition rate to 75%.
Keywords :
biomechanics; electromyography; genetic algorithms; learning (artificial intelligence); least mean squares methods; medical signal processing; pattern classification; radial basis function networks; signal classification; LMS algorithm; RBF neural network classifier; action recognition rate; adaptive genetic algorithm; electromyography; hand motions; multimotion pattern recognition; neural network system; nueral network hidden nodes; optimization; power spectrum K value method; surface EMG signal; Electromyography; Encoding; Genetic algorithms; Genetics; Neural networks; Training; Wrist; RBF neural network; Signal; Surface Electromyography; genetic algorithm; pattern recognition;
Conference_Titel :
Instrumentation, Measurement, Computer, Communication and Control (IMCCC), 2012 Second International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4673-5034-1
DOI :
10.1109/IMCCC.2012.46
