Electromyogram signal based human emotion classification using KNN and LDA
Proceeding of The IEEE International Conference on System Engineering and Technology, (ICSET 2011) at Shah Alam, Malaysia on 27 June 2011 through 28 June 2011. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp
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2014
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my.unimap-346562014-05-22T10:39:46Z Electromyogram signal based human emotion classification using KNN and LDA Murugappan, M., Dr. murugappan@unimap.edu.my Discrete wavelet transform EMG Emotions K Nearest Neighbor Linear Discriminant Analysis Proceeding of The IEEE International Conference on System Engineering and Technology, (ICSET 2011) at Shah Alam, Malaysia on 27 June 2011 through 28 June 2011. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp In this paper, we presents Electromyogram (EMG) signal based human emotion classification using K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA). Five most dominating emotions such as: happy, disgust, fear, sad and neutral are considered and these emotions are induced through Audio-visual stimuli (video clips). EMG signals are obtained by using 3 electrodes over 10 trials per emotion and preprocessed by using Butterworth 6th order filter to remove noises and external interferences. EMG signals on decomposed into four different frequency ranges ((8 Hz-16 Hz), (16 Hz-31 Hz) and (16 Hz-63 Hz)) using Discrete Wavelet Transform (DWT). The ststistical features extracted from the above frequency bands are mapped into five different emotions using two simple classifiers such as KNN and LDA. The value of K in KNN is varied randomly, and maximum classification rate is achieved at K=3. KNN classifier gives the highest classification rate on four emotions (disgust, happy, fear and neutral) different emotions and LDA on sad emotion. The maximum classification rate of disgust, happy, fear neutral, and sad are 90.83%, 100%, 94.17%, and 90.28% and 43.89%, respectively are achieved using KNN and LDA. The results from the proposed methodology are promising and female are easily evoked by different emotional stimuli compared to male. 2014-05-22T10:39:46Z 2014-05-22T10:39:46Z 2011-06 Working Paper p. 106-110 http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5993430 http://dspace.unimap.edu.my:80/dspace/handle/123456789/34656 http://dx.doi.org/10.1109/ICSEngT.2011.5993430 978-1-4577-1256-2 en Proceeding of The IEEE International Conference on System Engineering and Technology (ICSET 2011); IEEE Conference Publications |
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Universiti Malaysia Perlis |
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| continent |
Asia |
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Malaysia |
| content_provider |
Universiti Malaysia Perlis |
| content_source |
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| url_provider |
http://dspace.unimap.edu.my/ |
| language |
English |
| topic |
Discrete wavelet transform EMG Emotions K Nearest Neighbor Linear Discriminant Analysis |
| spellingShingle |
Discrete wavelet transform EMG Emotions K Nearest Neighbor Linear Discriminant Analysis Murugappan, M., Dr. Electromyogram signal based human emotion classification using KNN and LDA |
| description |
Proceeding of The IEEE International Conference on System Engineering and Technology, (ICSET 2011) at Shah Alam, Malaysia on 27 June 2011 through 28 June 2011. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp |
| author2 |
murugappan@unimap.edu.my |
| author_facet |
murugappan@unimap.edu.my Murugappan, M., Dr. |
| format |
Working Paper |
| author |
Murugappan, M., Dr. |
| author_sort |
Murugappan, M., Dr. |
| title |
Electromyogram signal based human emotion classification using KNN and LDA |
| title_short |
Electromyogram signal based human emotion classification using KNN and LDA |
| title_full |
Electromyogram signal based human emotion classification using KNN and LDA |
| title_fullStr |
Electromyogram signal based human emotion classification using KNN and LDA |
| title_full_unstemmed |
Electromyogram signal based human emotion classification using KNN and LDA |
| title_sort |
electromyogram signal based human emotion classification using knn and lda |
| publisher |
IEEE Conference Publications |
| publishDate |
2014 |
| url |
http://dspace.unimap.edu.my:80/dspace/handle/123456789/34656 |
| _version_ |
1643797556829880320 |