Electromagnetic based emotion recognition using ANOVA feature selection and bayes network
The paper discusses the development of emotion recognition system which can be applied to a wider range of human population. This is achieved by measuring the unique electromagnetic (EM) signal generated upon invoking certain emotions. A set of audio-visual stimulants is designed to invoke the des...
| Main Authors: | , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/39795/ http://irep.iium.edu.my/39795/1/39795.pdf http://irep.iium.edu.my/39795/4/49575_Electric%20vehicle%20battery%20modelling%20and%20performance_Scopus.pdf |
| _version_ | 1848781844721434624 |
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| author | Ghazali, Aimi Shazwani Sidek, Shahrul Na'im |
| author_facet | Ghazali, Aimi Shazwani Sidek, Shahrul Na'im |
| author_sort | Ghazali, Aimi Shazwani |
| building | IIUM Repository |
| collection | Online Access |
| description | The paper discusses the development of emotion
recognition system which can be applied to a wider range of human population. This is achieved by measuring the unique electromagnetic (EM) signal generated upon invoking certain emotions. A set of audio-visual stimulants is designed to invoke the desired emotions under study that are happy, sad and nervous. A set of questionnaire is developed to verify the
stimulant effectiveness in invoking the emotion. The
recognition of the emotion is deduced from the measured
electromagnetic signals radiated from the human body by a
handheld device called Resonant Field Imaging (RFITM). There are ten points of interest (POIs) on the body where the signals are measured to form the dataset which later fed into Bayes Network (BN) to classify the emotion. ANOVA test is run in selecting the best features to classify the emotions. The result after eliminating 6 from 10 POIs demonstrates the system performance is not compromised. The efficiency of ANOVA and BN in selecting the best features to model the emotion recognition system has successfully optimized the cost of the system and reduced the time to measure the signals quite significantly. |
| first_indexed | 2025-11-14T15:56:01Z |
| format | Proceeding Paper |
| id | iium-39795 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T15:56:01Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-397952019-01-10T05:02:07Z http://irep.iium.edu.my/39795/ Electromagnetic based emotion recognition using ANOVA feature selection and bayes network Ghazali, Aimi Shazwani Sidek, Shahrul Na'im TA164 Bioengineering The paper discusses the development of emotion recognition system which can be applied to a wider range of human population. This is achieved by measuring the unique electromagnetic (EM) signal generated upon invoking certain emotions. A set of audio-visual stimulants is designed to invoke the desired emotions under study that are happy, sad and nervous. A set of questionnaire is developed to verify the stimulant effectiveness in invoking the emotion. The recognition of the emotion is deduced from the measured electromagnetic signals radiated from the human body by a handheld device called Resonant Field Imaging (RFITM). There are ten points of interest (POIs) on the body where the signals are measured to form the dataset which later fed into Bayes Network (BN) to classify the emotion. ANOVA test is run in selecting the best features to classify the emotions. The result after eliminating 6 from 10 POIs demonstrates the system performance is not compromised. The efficiency of ANOVA and BN in selecting the best features to model the emotion recognition system has successfully optimized the cost of the system and reduced the time to measure the signals quite significantly. 2014 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/39795/1/39795.pdf application/pdf en http://irep.iium.edu.my/39795/4/49575_Electric%20vehicle%20battery%20modelling%20and%20performance_Scopus.pdf Ghazali, Aimi Shazwani and Sidek, Shahrul Na'im (2014) Electromagnetic based emotion recognition using ANOVA feature selection and bayes network. In: 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES 2014), 8-10 December 2014, Miri, Sarawak. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7047556 |
| spellingShingle | TA164 Bioengineering Ghazali, Aimi Shazwani Sidek, Shahrul Na'im Electromagnetic based emotion recognition using ANOVA feature selection and bayes network |
| title | Electromagnetic based emotion recognition using ANOVA feature selection and bayes network |
| title_full | Electromagnetic based emotion recognition using ANOVA feature selection and bayes network |
| title_fullStr | Electromagnetic based emotion recognition using ANOVA feature selection and bayes network |
| title_full_unstemmed | Electromagnetic based emotion recognition using ANOVA feature selection and bayes network |
| title_short | Electromagnetic based emotion recognition using ANOVA feature selection and bayes network |
| title_sort | electromagnetic based emotion recognition using anova feature selection and bayes network |
| topic | TA164 Bioengineering |
| url | http://irep.iium.edu.my/39795/ http://irep.iium.edu.my/39795/ http://irep.iium.edu.my/39795/1/39795.pdf http://irep.iium.edu.my/39795/4/49575_Electric%20vehicle%20battery%20modelling%20and%20performance_Scopus.pdf |