Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/34595
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dc.contributor.authorZheng, Bong Siao
dc.contributor.authorMurugappan, M., Dr.
dc.contributor.authorSazali, Yaacob, Prof. Dr.
dc.date.accessioned2014-05-21T07:51:10Z
dc.date.available2014-05-21T07:51:10Z
dc.date.issued2012-09
dc.identifier.citationp. 293-298en_US
dc.identifier.isbn978-1-4673-3004-6
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6496647
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/34595
dc.descriptionProceeding of The IEEE Symposium on Industrial Electronics and Applications (ISIEA 2012) at Bandung, Indonesia on 23 September 2012 until 26 September 2012. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jspen_US
dc.description.abstractContinuous existence of negative emotions (disgust, anger, fear and sad) over a longer period of time induces emotional stress. This emotional stress can be analyzed through physiological signal characteristics such as Electrocardiogram (ECG), Electromyogram (EMG), etc. In this work, we have proposed a customized protocol experiment to induce emotional stress through audio-visual stimuli (video clips) and simultaneously acquired ECG signals. ECG signals are preprocessed using Elliptic filter and Discrete Wavelet Transform (DWT). Heart Rate Variability (HRV) signals is derived from ECG signals through QRS detection algorithm. Heart rate (HR) is used as a statistical feature to distinguish the emotional stress through a nonlinear classifier (K Nearest Neighbor (KNN)) into three different classes namely, negative emotions, positive emotions (surprise and happy) and neutral. We have analyzed the HRV signals based on segmenting the data into 5 and 10 segments. The maximum classification rate of 93.1% on positive emotion, 85.1% on negative emotion and 71.7% on neutral state is achieved using KNN. Indeed, the negative emotions are further categorized as emotional stress and emotional non-stress and achieved a maximum classification rate of 82.9% and 86.9%, respectively. This accuracy proved that the customized protocol experiment is successful in inducing emotional stress among subjects.en_US
dc.language.isoenen_US
dc.publisherIEEE Conference Publicationsen_US
dc.relation.ispartofseriesProceeding of The IEEE Symposium on Industrial Electronics and Applications (ISIEA 2012);
dc.subjectElectrocardiogram (ECG)en_US
dc.subjectEmotional stressen_US
dc.subjectHeart Rate Variability (HRV)en_US
dc.subjectK-Nearest Neighbor (KNN)en_US
dc.titleHuman emotional stress assessment through Heart Rate Detection in a customized protocol experimenten_US
dc.typeWorking Paperen_US
dc.identifier.urlhttp://dx.doi.org/10.1109/ISIEA.2012.6496647
dc.contributor.urlwendy880806@gmail.comen_US
dc.contributor.urlmurugappan@unimap.edu.myen_US
dc.contributor.urls.yaacob@unimap.edu.myen_US
Appears in Collections:Sazali Yaacob, Prof. Dr.
M. Murugappan, Dr.



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