Emotion Recognition from Surface EMG Signal Using Wavelet Transform and Neural Network
Emotion recognition is a pivotal question of affective computing. This paper adopts the wavelet transform to analyse the surface EMG signal instability feature. Surface EMG signal is decomposed by discrete wavelet transform (DWT) and selected maximum and minimum of the wavelet coefficients in every level. The extracted maximum and minimum of the wavelet coefficients is inputted to identify emotion by the BP neural network improved by Levenberg-Marquardt algorithm. Experimental result shows that identification purpose of four emotional signals (joy, anger, sadness and pleasure) is effective and have are a great potential in practical application of emotion recognition.
affective computing emotion recognition wavelet transform BP Neural Network EMG
Bo Cheng Guangyuan Liu
Department of Business Administration Chongqing Three Gorges University Chongqing, China School of Electronic and Information Engineering Southwest University ChongqingChina
国际会议
上海
英文
1363-1366
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）