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https://olympias.lib.uoi.gr/jspui/handle/123456789/11361Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Rigas, G. | en |
| dc.contributor.author | Goletsis, Y. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.date.accessioned | 2015-11-24T17:05:40Z | - |
| dc.date.available | 2015-11-24T17:05:40Z | - |
| dc.identifier.issn | 1524-9050 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11361 | - |
| dc.rights | Default Licence | - |
| dc.subject | bayesian networks (bns) | en |
| dc.subject | driver stress | en |
| dc.subject | driving environment | en |
| dc.subject | kalman filter | en |
| dc.subject | physiological signals | en |
| dc.subject | signals | en |
| dc.title | Real-Time Driver's Stress Event Detection | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | Doi 10.1109/Tits.2011.2168215 | - |
| heal.identifier.secondary | <Go to ISI>://000300846700021 | - |
| heal.identifier.secondary | http://ieeexplore.ieee.org/ielx5/6979/6157683/06036175.pdf?tp=&arnumber=6036175&isnumber=6157683 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Οικονομικών και Κοινωνικών Επιστημών. Τμήμα Οικονομικών Επιστημών | el |
| heal.publicationDate | 2012 | - |
| heal.abstract | In this paper, a real-time methodology for the detection of stress events while driving is presented. The detection is based on the use of physiological signals, i.e., electrocardiogram, electrodermal activity, and respiration, as well as past observations of driving behavior. Features are calculated over windows of specific length and are introduced in a Bayesian network to detect driver's stress events. The accuracy of the stress event detection based only on physiological features, evaluated on a data set obtained in real driving conditions, resulted in an accuracy of 82%. Enhancement of the stress event detection model with the incorporation of driving event information has reduced false positives, yielding an increased accuracy of 96%. Furthermore, our methodology demonstrates good adaptability due to the application of online learning of the model parameters. | en |
| heal.journalName | Ieee Transactions on Intelligent Transportation Systems | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) - ΟΕ | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Rigas-2012-Real-Time Driver's S.pdf | 575.14 kB | Adobe PDF | View/Open Request a copy |
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