Comparison of Multiple Physiological Sensors to Classify Operator State in Adaptive Automation Systems

Comparison of Multiple Physiological Sensors to Classify Operator State in Adaptive Automation Systems

Automating tasks alleviates operator resources to be delegated to other demands, but the cost is often situation awareness. In contrast, complete manual control of a system opens the door for greater human error. Therefore, an ideal situation would require the development of an adaptive system in wh...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2010-09, Vol.54 (3), p.195-199
Hauptverfasser: Taylor, Grant, Reinerman-Jones, Lauren, Cosenzo, Keryl, Nicholson, Denise
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 199
container_issue 3
container_start_page 195
container_title Proceedings of the Human Factors and Ergonomics Society Annual Meeting
container_volume 54
creator Taylor, Grant
Reinerman-Jones, Lauren
Cosenzo, Keryl
Nicholson, Denise
description Automating tasks alleviates operator resources to be delegated to other demands, but the cost is often situation awareness. In contrast, complete manual control of a system opens the door for greater human error. Therefore, an ideal situation would require the development of an adaptive system in which automation can be triggered based on performance of a particular task, time spent on the task, or perhaps physiological response. The latter pertains to the goal for this particular study. Electroencephalogram (EEG), electrocardiogram (ECG), and eye tracking measures were recorded during six multi-tasking scenarios to assess if any one single measure is best suited for future implementation as an automation invocation. EEG showed the greatest potential for that purpose.
doi_str_mv 10.1177/154193121005400302
format Article
fullrecord <record><control><sourceid>sage_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1177_154193121005400302</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sage_id>10.1177_154193121005400302</sage_id><sourcerecordid>10.1177_154193121005400302</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1322-636b486f47505a9f0e076432657869ddbcc60a37ea0930cd2156228068e24b643</originalsourceid><addsrcrecordid>eNp9kMtKw0AYhQdRsFZfwNW8QOw_92RZgjeoVKiuwzSZqVOSTJiZCHl7U-pOcHU233c4HITuCTwQotSKCE4KRigBEByAAb1AC0pkkQmQ6hItTkB2Iq7RTYxHAMoU4wtkS98NOrjoe-wtfhvb5IbW4PevKTrf-oOrdYt3po8-RJw8Llsdo7MT3g4m6OQD3iWdDHY9Xjd6SO7b4PWYfKeTmzt3U0ymi7foyuo2mrvfXKLPp8eP8iXbbJ9fy_UmqwmjNJNM7nkuLVcChC4sGFCSMyqFymXRNPu6lqCZMhoKBnVDiZCU5iBzQ_l-JpeInnvr4GMMxlZDcJ0OU0WgOj1V_X1qllZnKeqDqY5-DP288T_jB-iuaLA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Comparison of Multiple Physiological Sensors to Classify Operator State in Adaptive Automation Systems</title><source>Access via SAGE</source><creator>Taylor, Grant ; Reinerman-Jones, Lauren ; Cosenzo, Keryl ; Nicholson, Denise</creator><creatorcontrib>Taylor, Grant ; Reinerman-Jones, Lauren ; Cosenzo, Keryl ; Nicholson, Denise</creatorcontrib><description>Automating tasks alleviates operator resources to be delegated to other demands, but the cost is often situation awareness. In contrast, complete manual control of a system opens the door for greater human error. Therefore, an ideal situation would require the development of an adaptive system in which automation can be triggered based on performance of a particular task, time spent on the task, or perhaps physiological response. The latter pertains to the goal for this particular study. Electroencephalogram (EEG), electrocardiogram (ECG), and eye tracking measures were recorded during six multi-tasking scenarios to assess if any one single measure is best suited for future implementation as an automation invocation. EEG showed the greatest potential for that purpose.</description><identifier>ISSN: 1541-9312</identifier><identifier>ISSN: 1071-1813</identifier><identifier>EISSN: 2169-5067</identifier><identifier>DOI: 10.1177/154193121005400302</identifier><language>eng</language><publisher>Los Angeles, CA: SAGE Publications</publisher><ispartof>Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2010-09, Vol.54 (3), p.195-199</ispartof><rights>2010 Human Factors and Ergonomics Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1322-636b486f47505a9f0e076432657869ddbcc60a37ea0930cd2156228068e24b643</citedby><cites>FETCH-LOGICAL-c1322-636b486f47505a9f0e076432657869ddbcc60a37ea0930cd2156228068e24b643</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1177/154193121005400302$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1177/154193121005400302$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>314,780,784,21819,27924,27925,43621,43622</link.rule.ids></links><search><creatorcontrib>Taylor, Grant</creatorcontrib><creatorcontrib>Reinerman-Jones, Lauren</creatorcontrib><creatorcontrib>Cosenzo, Keryl</creatorcontrib><creatorcontrib>Nicholson, Denise</creatorcontrib><title>Comparison of Multiple Physiological Sensors to Classify Operator State in Adaptive Automation Systems</title><title>Proceedings of the Human Factors and Ergonomics Society Annual Meeting</title><description>Automating tasks alleviates operator resources to be delegated to other demands, but the cost is often situation awareness. In contrast, complete manual control of a system opens the door for greater human error. Therefore, an ideal situation would require the development of an adaptive system in which automation can be triggered based on performance of a particular task, time spent on the task, or perhaps physiological response. The latter pertains to the goal for this particular study. Electroencephalogram (EEG), electrocardiogram (ECG), and eye tracking measures were recorded during six multi-tasking scenarios to assess if any one single measure is best suited for future implementation as an automation invocation. EEG showed the greatest potential for that purpose.</description><issn>1541-9312</issn><issn>1071-1813</issn><issn>2169-5067</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNp9kMtKw0AYhQdRsFZfwNW8QOw_92RZgjeoVKiuwzSZqVOSTJiZCHl7U-pOcHU233c4HITuCTwQotSKCE4KRigBEByAAb1AC0pkkQmQ6hItTkB2Iq7RTYxHAMoU4wtkS98NOrjoe-wtfhvb5IbW4PevKTrf-oOrdYt3po8-RJw8Llsdo7MT3g4m6OQD3iWdDHY9Xjd6SO7b4PWYfKeTmzt3U0ymi7foyuo2mrvfXKLPp8eP8iXbbJ9fy_UmqwmjNJNM7nkuLVcChC4sGFCSMyqFymXRNPu6lqCZMhoKBnVDiZCU5iBzQ_l-JpeInnvr4GMMxlZDcJ0OU0WgOj1V_X1qllZnKeqDqY5-DP288T_jB-iuaLA</recordid><startdate>201009</startdate><enddate>201009</enddate><creator>Taylor, Grant</creator><creator>Reinerman-Jones, Lauren</creator><creator>Cosenzo, Keryl</creator><creator>Nicholson, Denise</creator><general>SAGE Publications</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>201009</creationdate><title>Comparison of Multiple Physiological Sensors to Classify Operator State in Adaptive Automation Systems</title><author>Taylor, Grant ; Reinerman-Jones, Lauren ; Cosenzo, Keryl ; Nicholson, Denise</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1322-636b486f47505a9f0e076432657869ddbcc60a37ea0930cd2156228068e24b643</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Taylor, Grant</creatorcontrib><creatorcontrib>Reinerman-Jones, Lauren</creatorcontrib><creatorcontrib>Cosenzo, Keryl</creatorcontrib><creatorcontrib>Nicholson, Denise</creatorcontrib><collection>CrossRef</collection><jtitle>Proceedings of the Human Factors and Ergonomics Society Annual Meeting</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Taylor, Grant</au><au>Reinerman-Jones, Lauren</au><au>Cosenzo, Keryl</au><au>Nicholson, Denise</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of Multiple Physiological Sensors to Classify Operator State in Adaptive Automation Systems</atitle><jtitle>Proceedings of the Human Factors and Ergonomics Society Annual Meeting</jtitle><date>2010-09</date><risdate>2010</risdate><volume>54</volume><issue>3</issue><spage>195</spage><epage>199</epage><pages>195-199</pages><issn>1541-9312</issn><issn>1071-1813</issn><eissn>2169-5067</eissn><abstract>Automating tasks alleviates operator resources to be delegated to other demands, but the cost is often situation awareness. In contrast, complete manual control of a system opens the door for greater human error. Therefore, an ideal situation would require the development of an adaptive system in which automation can be triggered based on performance of a particular task, time spent on the task, or perhaps physiological response. The latter pertains to the goal for this particular study. Electroencephalogram (EEG), electrocardiogram (ECG), and eye tracking measures were recorded during six multi-tasking scenarios to assess if any one single measure is best suited for future implementation as an automation invocation. EEG showed the greatest potential for that purpose.</abstract><cop>Los Angeles, CA</cop><pub>SAGE Publications</pub><doi>10.1177/154193121005400302</doi><tpages>5</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1541-9312
ispartof Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2010-09, Vol.54 (3), p.195-199
issn 1541-9312
1071-1813
2169-5067
language eng
recordid cdi_crossref_primary_10_1177_154193121005400302
source Access via SAGE
title Comparison of Multiple Physiological Sensors to Classify Operator State in Adaptive Automation Systems
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-20T17%3A23%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-sage_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Comparison%20of%20Multiple%20Physiological%20Sensors%20to%20Classify%20Operator%20State%20in%20Adaptive%20Automation%20Systems&rft.jtitle=Proceedings%20of%20the%20Human%20Factors%20and%20Ergonomics%20Society%20Annual%20Meeting&rft.au=Taylor,%20Grant&rft.date=2010-09&rft.volume=54&rft.issue=3&rft.spage=195&rft.epage=199&rft.pages=195-199&rft.issn=1541-9312&rft.eissn=2169-5067&rft_id=info:doi/10.1177/154193121005400302&rft_dat=%3Csage_cross%3E10.1177_154193121005400302%3C/sage_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_sage_id=10.1177_154193121005400302&rfr_iscdi=true