Forearm muscle electromyogram signal analysis for real time detection of finger movements

Publish Year: 1392
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:

TIAU01_079

تاریخ نمایه سازی: 14 شهریور 1393

Abstract:

In this paper, a nonlinear classification method based on the neural networks is presented for detection of different finger movements. Forearm muscles SEMG signal during different the finger movements were recorded. Then, the intended movement is detected through a presented signalanalyses method. In the proposed methodology, the preprocessed SEMG signals were segmented.Then, the extracted features related to each segment were used to classify the intended movement. Theneural networks were used as the nonlinear classifiers to discriminate the 11 different movements. Theperformance of neural networks was compared with the performance of a linear classifier called SVM. According to the results, the performance of neural network in offline and online mode are 90.57% and 98.14%, respectively, and performance of SVM classifier in offline and mode is 83.58%.

Keywords:

electromyography , neural network , support vector machine (SVM) , hand prostheses

Authors

AMIRAHMAD KHANIAN

Islamic azad university mashhad branch, biomedical engineering group, mashhad, Iran.

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