ARTIFICIAL NEURAL NETWORKS APPLIED TO THE CLASSIFICATION OF HAND GESTURES USING ELETROMYOGRAPHIC SIGNALS

被引：0
|
作者：
Fonseca, M. G. B. [1 ]
Conceicao, A. G. S. [1 ]
Simas Filho, E. F. [1 ]
机构：
[1] Univ Fed Bahia, Elect Engn Dept, Salvador, BA, Brazil
来源：
2017 LATIN AMERICAN ROBOTICS SYMPOSIUM (LARS) AND 2017 BRAZILIAN SYMPOSIUM ON ROBOTICS (SBR) | 2017年
关键词：
Electromyography Signals; Artificial Neural Network; Pattern Recognition; Classification of Gestures; Human-robot interaction;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper aims at the classification of hand gestures using electromyographic signals (EMG) obtained through a Myo (TM) armband, which has eight medical grade electrodes. Each electrode provides information regarding muscles contraction performed during the execution of the movement. From these electrodes signals are extracted seven features for each one of eight electrodes. After extraction of the characteristics a Feedforward Artificial Neural Network is trained to recognize the desired classes. The motivation of this research is the recognition of gestures for human-robot interaction. Experimental results are presented to demonstrate the performance of the proposed method.
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页数：6
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