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https://olympias.lib.uoi.gr/jspui/handle/123456789/13614Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Katsis, C. D. | en |
| dc.contributor.author | Katertsidis, N. | en |
| dc.contributor.author | Ganiatsas, G. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.date.accessioned | 2015-11-24T17:31:42Z | - |
| dc.date.available | 2015-11-24T17:31:42Z | - |
| dc.identifier.issn | 1083-4427 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13614 | - |
| dc.rights | Default Licence | - |
| dc.subject | adaptive neuro-fuzzy inference system (anfis) | en |
| dc.subject | biosignal processing | en |
| dc.subject | emotion recognition | en |
| dc.subject | support vector machines (svms) | en |
| dc.subject | wearable system | en |
| dc.subject | physiological signals | en |
| dc.subject | facial expressions | en |
| dc.title | Toward emotion recognition in car-racing drivers: A biosignal processing approach | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | Doi 10.1109/Tsmca.2008.918624 | - |
| heal.identifier.secondary | <Go to ISI>://000258183000001 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.publicationDate | 2008 | - |
| heal.abstract | In this paper, we present a methodology and a wearable system for the evaluation of the emotional states of car-racing drivers. The proposed approach performs an assessment of the emotional states using facial electromyograms, electrocardiogram, respiration, and electrodermal activity. The system consists of the following: 1) the multisensorial wearable module; 2) the centralized computing module; and 3) the system's interface. The system has been preliminary validated by using data obtained from ten subjects in simulated racing conditions. The emotional classes identified are high stress, low stress, disappointment, and euphoria. Support vector machines (SVMs) and adaptive neuro-fuzzy inference system (ANFIS) have been used for the classification. The overall classification rates achieved by using tenfold cross validation are 79.3% and 76.7% for the SVM and the ANFIS, respectively. | en |
| heal.journalName | Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Fotiadis-2008-toward emotion recognition.pdf | 619.27 kB | Adobe PDF | View/Open Request a copy |
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