Real-Time Driver's Stress Event Detection
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 calculate...
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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2012-03, Vol.13 (1), p.221-234 |
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| creator | Rigas, G. Goletsis, Y. Fotiadis, D. I. |
| description | 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. |
| doi_str_mv | 10.1109/TITS.2011.2168215 |
| format | Article |
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Furthermore, our methodology demonstrates good adaptability due to the application of online learning of the model parameters.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2011.2168215</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Accuracy ; Bayesian networks (BNs) ; driver stress ; Driving ; Driving conditions ; driving environment ; Estimation ; Feature extraction ; Heart rate variability ; Kalman filter ; Kalman filters ; Mathematical models ; Methodology ; On-line systems ; physiological signals ; Real time ; Real time systems ; Stress ; Stresses ; Vehicles</subject><ispartof>IEEE transactions on intelligent transportation systems, 2012-03, Vol.13 (1), p.221-234</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Mar 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-a272cdac392d3e2d2f710c8e3069dc0fc5bec7f4dc059006cc2b576e555ad7473</citedby><cites>FETCH-LOGICAL-c325t-a272cdac392d3e2d2f710c8e3069dc0fc5bec7f4dc059006cc2b576e555ad7473</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6036175$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,793,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6036175$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Rigas, G.</creatorcontrib><creatorcontrib>Goletsis, Y.</creatorcontrib><creatorcontrib>Fotiadis, D. I.</creatorcontrib><title>Real-Time Driver's Stress Event Detection</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>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.</description><subject>Accuracy</subject><subject>Bayesian networks (BNs)</subject><subject>driver stress</subject><subject>Driving</subject><subject>Driving conditions</subject><subject>driving environment</subject><subject>Estimation</subject><subject>Feature extraction</subject><subject>Heart rate variability</subject><subject>Kalman filter</subject><subject>Kalman filters</subject><subject>Mathematical models</subject><subject>Methodology</subject><subject>On-line systems</subject><subject>physiological signals</subject><subject>Real time</subject><subject>Real time systems</subject><subject>Stress</subject><subject>Stresses</subject><subject>Vehicles</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkMFKw0AQhhdRsFYfQLwEL-ohcWc3s9scpa1aKAg2npd0M4GUNKm7acG3d0OLB08zDN_8_HyM3QJPAHj2nC_yVSI4QCJATQTgGRsB4iTmHNT5sIs0zjjyS3bl_SZcUwQYsadPKpo4r7cUzVx9IPfgo1XvyPtofqC2j2bUk-3rrr1mF1XReLo5zTH7ep3n0_d4-fG2mL4sYysF9nEhtLBlYWUmSkmiFJUGbickucpKyyuLa7K6SsOOGefKWrFGrQgRi1KnWo7Z4zF357rvPfnebGtvqWmKlrq9N8AhU6hRQEDv_6Gbbu_a0M5kIhQIIlSA4AhZ13nvqDI7V28L9xOSzODODO7M4M6c3IWfu-NPTUR_vOJSgUb5C1rNaF8</recordid><startdate>201203</startdate><enddate>201203</enddate><creator>Rigas, G.</creator><creator>Goletsis, Y.</creator><creator>Fotiadis, D. 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I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real-Time Driver's Stress Event Detection</atitle><jtitle>IEEE transactions on intelligent transportation systems</jtitle><stitle>TITS</stitle><date>2012-03</date><risdate>2012</risdate><volume>13</volume><issue>1</issue><spage>221</spage><epage>234</epage><pages>221-234</pages><issn>1524-9050</issn><eissn>1558-0016</eissn><coden>ITISFG</coden><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.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TITS.2011.2168215</doi><tpages>14</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Accuracy Bayesian networks (BNs) driver stress Driving Driving conditions driving environment Estimation Feature extraction Heart rate variability Kalman filter Kalman filters Mathematical models Methodology On-line systems physiological signals Real time Real time systems Stress Stresses Vehicles |
| title | Real-Time Driver's Stress Event Detection |
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