Neural Network Based Classification of Human Emotions using Electromyogram Signals

被引：0
|
作者：
Latha, Charlyn Pushpa G. [1 ]
Hema, C. R. [1 ]
Paulraj, M. P. [2 ]
机构：
[1] Karpagam Univ, Fac Engn, Coimbatore, Tamil Nadu, India
[2] Univ Malaysia Perlis, Sch Mechatron Engn, Perlis, Malaysia
来源：
PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING & COMMUNICATION SYSTEMS (ICACCS) | 2013年
关键词：
Electromyography; Facial Electromyography; Bandpower; Feed Forward Neural Network; Elman Neural Network;
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：
Facial expression of emotion is of great interest to many researchers. Facial Electromyography (FEMG) is used for the identification of different facial expressions namely happy, sad, fear, neutral, surprise etc. In this paper, a simple algorithm to identify six emotions using the FEMG signals is proposed. FEMG signals are recorded from seven subjects. The six emotions are identified using bandpower features extracted from the raw FEMG signals and neural networks. In this study, two networks are used to identify the emotions. The network has an average classification accuracy of 94.32%.
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页数：4
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