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...

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Main Authors:	Ghazali, Aimi Shazwani, Sidek, Shahrul Na'im
Format:	Conference or Workshop Item
Language:	English English
Published:	2014
Subjects:	TA164 Bioengineering
Online Access:	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

id	iium-39795
recordtype	eprints
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 Conference or Workshop Item 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
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TA164 Bioengineering
spellingShingle	TA164 Bioengineering Ghazali, Aimi Shazwani Sidek, Shahrul Na'im Electromagnetic based emotion recognition using ANOVA feature selection and bayes network
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.
format	Conference or Workshop Item
author	Ghazali, Aimi Shazwani Sidek, Shahrul Na'im
author_facet	Ghazali, Aimi Shazwani Sidek, Shahrul Na'im
author_sort	Ghazali, Aimi Shazwani
title	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_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_sort	electromagnetic based emotion recognition using anova feature selection and bayes network
publishDate	2014
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
first_indexed	2023-09-18T20:57:08Z
last_indexed	2023-09-18T20:57:08Z
_version_	1777410378969382912

Electromagnetic based emotion recognition using ANOVA feature selection and bayes network

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