Non-invasive, non-contact based affective state identification
This paper discusses a study on detecting affective states of human subjects from their body’s electromagnetic (EM) wave. In particular, the affective states under investigation are happy, nervous, and sad which play important roles in Human-Robot Interaction (HRI)applications. A structured experi...
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iium-382422019-01-10T05:02:24Z http://irep.iium.edu.my/38242/ Non-invasive, non-contact based affective state identification Ghazali, Aimi Shazwani Sidek, Shahrul Na'im TA164 Bioengineering This paper discusses a study on detecting affective states of human subjects from their body’s electromagnetic (EM) wave. In particular, the affective states under investigation are happy, nervous, and sad which play important roles in Human-Robot Interaction (HRI)applications. A structured experimental setup was designed to invoke the desired affective states. These states are induced by exposing the subject to a specific set of audiovisual stimulations upon which the EM waves are captured from ten different regions of the subject’s body by using a handheld device called Resonant Field Imaging (RFITM). Nine subjects are randomly chosen and the collected data are then preprocessed and trained by Bayesian Network (BN) to map the EM wave to the corresponding affective states. Preliminary results demonstrate the ability of the BN to predict human affective state with 80.6% precision, and 90% accuracy. 2014 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/38242/1/ISCAIE2014_official_paper.pdf application/pdf en http://irep.iium.edu.my/38242/4/38242_Non-invasive%2C%20non-contact%20based%20affective%20state_Scopus.pdf Ghazali, Aimi Shazwani and Sidek, Shahrul Na'im (2014) Non-invasive, non-contact based affective state identification. In: 2014 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), 7-8 Apr. 2014, Penang, Malaysia. http://myies.org/iscaie2014/index.php/contact-us/2-uncategorised |
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TA164 Bioengineering |
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TA164 Bioengineering Ghazali, Aimi Shazwani Sidek, Shahrul Na'im Non-invasive, non-contact based affective state identification |
description |
This paper discusses a study on detecting
affective states of human subjects from their body’s
electromagnetic (EM) wave. In particular, the affective states under investigation are happy, nervous, and sad which play important roles in Human-Robot Interaction (HRI)applications. A structured experimental setup was designed to invoke the desired affective states. These states are induced by exposing the subject to a specific set of audiovisual stimulations upon which the EM waves are captured from ten different regions of the subject’s body by using a handheld device called Resonant Field Imaging (RFITM). Nine subjects are randomly chosen and the collected data are then preprocessed and trained by Bayesian Network (BN) to map the EM wave to the
corresponding affective states. Preliminary results
demonstrate the ability of the BN to predict human affective state with 80.6% precision, and 90% accuracy. |
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 |
Non-invasive, non-contact based affective state
identification |
title_short |
Non-invasive, non-contact based affective state
identification |
title_full |
Non-invasive, non-contact based affective state
identification |
title_fullStr |
Non-invasive, non-contact based affective state
identification |
title_full_unstemmed |
Non-invasive, non-contact based affective state
identification |
title_sort |
non-invasive, non-contact based affective state
identification |
publishDate |
2014 |
url |
http://irep.iium.edu.my/38242/ http://irep.iium.edu.my/38242/ http://irep.iium.edu.my/38242/1/ISCAIE2014_official_paper.pdf http://irep.iium.edu.my/38242/4/38242_Non-invasive%2C%20non-contact%20based%20affective%20state_Scopus.pdf |
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2023-09-18T20:54:55Z |
last_indexed |
2023-09-18T20:54:55Z |
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1777410240256409600 |