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dc.contributor.authorSelvaraj, Jerritta
dc.contributor.authorMurugappan, Muthusamy, Dr.
dc.contributor.authorWan Khairunizam, Wan Ahmad, Dr.
dc.contributor.authorSazali, Yaacob, Prof. Dr.
dc.date.accessioned2014-03-28T02:58:40Z
dc.date.available2014-03-28T02:58:40Z
dc.date.issued2014-01
dc.identifier.citationBiomedizinische Technik/Biomedical Engineering, January 2014, pages 1–9en_US
dc.identifier.issn1862-278X (Online)
dc.identifier.issn0013-5585 (Print)
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/33184
dc.descriptionLink to publisher's homepage http://www.degruyter.com/en_US
dc.description.abstractEmotional intelligence is one of the key research areas in human-computer interaction. This paper reports the development of an emotion recognition system using facial electromyogram (EMG) signals focusing the ambiguity on the frequency ranges used by different research works. The six emotional states (happiness, sadness, fear, surprise, disgust, and neutral) were elicited in 60 subjects using audio visual stimuli. Statistical features were extracted from the signals at high, medium, low, and very low frequency levels. They were then classified using four classifiers – naïve Bayes, regression tree, K-nearest neighbor, and fuzzy K-nearest neighbor, and the performance of the system at the different frequency levels were studied using three metrics, namely, % accuracy, sensitivity, and specificity. The post hoctests in analysis of variance (ANOVA) indicate that the features contain significant emotional information at the very low-frequency range (<0.08 Hz). Similarly, the performance metrics of the classifiers also ensure better recognition rate at very low-frequency range. Though this range of frequency has not been used by researchers, the results of this work indicate that it should not be ignored. Further investigation of the very low frequency range to identify emotional information is still in progress.en_US
dc.language.isoenen_US
dc.publisherWalter de Gruyter GmbHen_US
dc.subjectAnalysis of varianceen_US
dc.subjectAudio visual stimulien_US
dc.subjectEmotional frequency analysisen_US
dc.subjectHuman-computer interactionen_US
dc.subjectFacial electromyogram signalsen_US
dc.subjectSensitivityen_US
dc.subjectSpecificityen_US
dc.titleFrequency study of facial electromyography signals with respect to emotion recognitionen_US
dc.typeArticleen_US
dc.identifier.url10.1515/bmt-2013-0118
dc.identifier.urlhttp://www.degruyter.com/
dc.contributor.urlkhairunizam@unimap.edu.myen_US
dc.contributor.urlmurugappan@unimap.edu.my
dc.contributor.urls.yaacob@unimap.edu.my


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