Detection of driver drowsiness using wearable devices: A feasibility study of the proximity sensor
Drowsiness is one of the major factors that cause crashes in the transportation industry. Drowsiness detection systems can alert drowsy operators and potentially reduce the risk of crashes. In this study, a Google-Glass-based drowsiness detection system was developed and validated. The proximity sen...
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| Veröffentlicht in: | Applied ergonomics 2017-11, Vol.65, p.473-480 |
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| creator | He, Jibo Choi, William Yang, Yan Lu, Junshi Wu, Xiaohui Peng, Kaiping |
| description | Drowsiness is one of the major factors that cause crashes in the transportation industry. Drowsiness detection systems can alert drowsy operators and potentially reduce the risk of crashes. In this study, a Google-Glass-based drowsiness detection system was developed and validated.
The proximity sensor of Google Glass was used to monitor eye blink frequency. A simulated driving study was carried out to validate the system. Driving performance and eye blinks were compared between the two states of alertness and drowsiness while driving.
Drowsy drivers increased frequency of eye blinks, produced longer braking response time and increased lane deviation, compared to when they were alert. A threshold algorithm for proximity sensor can reliably detect eye blinks and proved the feasibility of using Google Glass to detect operator drowsiness.
This technology provides a new platform to detect operator drowsiness and has the potential to reduce drowsiness-related crashes in driving and aviation.
•The infrared proximity sensor can monitor eye blinks.•The thresholding algorithm can use proximity value to detect eye blinks.•Prolonged driving increases eye blink frequency.•Longtime driving impairs driving performance. |
| doi_str_mv | 10.1016/j.apergo.2017.02.016 |
| format | Article |
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The proximity sensor of Google Glass was used to monitor eye blink frequency. A simulated driving study was carried out to validate the system. Driving performance and eye blinks were compared between the two states of alertness and drowsiness while driving.
Drowsy drivers increased frequency of eye blinks, produced longer braking response time and increased lane deviation, compared to when they were alert. A threshold algorithm for proximity sensor can reliably detect eye blinks and proved the feasibility of using Google Glass to detect operator drowsiness.
This technology provides a new platform to detect operator drowsiness and has the potential to reduce drowsiness-related crashes in driving and aviation.
•The infrared proximity sensor can monitor eye blinks.•The thresholding algorithm can use proximity value to detect eye blinks.•Prolonged driving increases eye blink frequency.•Longtime driving impairs driving performance.</description><identifier>ISSN: 0003-6870</identifier><identifier>EISSN: 1872-9126</identifier><identifier>DOI: 10.1016/j.apergo.2017.02.016</identifier><identifier>PMID: 28420482</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Adult ; Algorithms ; Attention ; Automobile Driving - psychology ; Biosensing Techniques - instrumentation ; Biosensing Techniques - methods ; Blinking ; Driver drowsiness ; Eye Movement Measurements - instrumentation ; Eye Movements ; Feasibility Studies ; Female ; Humans ; Male ; Proximity sensor ; Sleep Stages - physiology ; Wearable device ; Wearable Electronic Devices ; Young Adult</subject><ispartof>Applied ergonomics, 2017-11, Vol.65, p.473-480</ispartof><rights>2017 Elsevier Ltd</rights><rights>Copyright © 2017 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-7922a593def02fe5487e57bb91969f59eb12e17c9c347b0de12fd21242aace9a3</citedby><cites>FETCH-LOGICAL-c476t-7922a593def02fe5487e57bb91969f59eb12e17c9c347b0de12fd21242aace9a3</cites><orcidid>0000-0002-5110-581X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0003687017300522$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28420482$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>He, Jibo</creatorcontrib><creatorcontrib>Choi, William</creatorcontrib><creatorcontrib>Yang, Yan</creatorcontrib><creatorcontrib>Lu, Junshi</creatorcontrib><creatorcontrib>Wu, Xiaohui</creatorcontrib><creatorcontrib>Peng, Kaiping</creatorcontrib><title>Detection of driver drowsiness using wearable devices: A feasibility study of the proximity sensor</title><title>Applied ergonomics</title><addtitle>Appl Ergon</addtitle><description>Drowsiness is one of the major factors that cause crashes in the transportation industry. Drowsiness detection systems can alert drowsy operators and potentially reduce the risk of crashes. In this study, a Google-Glass-based drowsiness detection system was developed and validated.
The proximity sensor of Google Glass was used to monitor eye blink frequency. A simulated driving study was carried out to validate the system. Driving performance and eye blinks were compared between the two states of alertness and drowsiness while driving.
Drowsy drivers increased frequency of eye blinks, produced longer braking response time and increased lane deviation, compared to when they were alert. A threshold algorithm for proximity sensor can reliably detect eye blinks and proved the feasibility of using Google Glass to detect operator drowsiness.
This technology provides a new platform to detect operator drowsiness and has the potential to reduce drowsiness-related crashes in driving and aviation.
•The infrared proximity sensor can monitor eye blinks.•The thresholding algorithm can use proximity value to detect eye blinks.•Prolonged driving increases eye blink frequency.•Longtime driving impairs driving performance.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Attention</subject><subject>Automobile Driving - psychology</subject><subject>Biosensing Techniques - instrumentation</subject><subject>Biosensing Techniques - methods</subject><subject>Blinking</subject><subject>Driver drowsiness</subject><subject>Eye Movement Measurements - instrumentation</subject><subject>Eye Movements</subject><subject>Feasibility Studies</subject><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>Proximity sensor</subject><subject>Sleep Stages - physiology</subject><subject>Wearable device</subject><subject>Wearable Electronic Devices</subject><subject>Young Adult</subject><issn>0003-6870</issn><issn>1872-9126</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kDtv2zAUhYkiQe24_QdFwTGLFJKiRLJDACPNCzCQJZkJirxKaciiS0pO_e9Dx2nGTAf34NzXh9APSkpKaHOxLs0W4nMoGaGiJKzM5hc0p1KwQlHWnKA5IaQqGinIDJ2ltM6l5LT-imZMcka4ZHPU_oYR7OjDgEOHXfQ7iFnCS_IDpISnrM_4BUw0bQ_Ywc5bSL_wEndgkm9978c9TuPk9ocB4x_A2xj--c2bDUMK8Rs67Uyf4Pu7LtDTzfXj1V2xeri9v1quCstFMxZCMWZqVTnoCOug5lJALdpWUdWorlbQUgZUWGUrLlrigLLOMco4M8aCMtUCnR_n5gP-TpBGvfHJQt-bAcKUNJVSiYbLqs5RfozaGFKK0Olt9BsT95oSfaCr1_pIVx_oasJ0NnPbz_cNU7sB99H0H2cOXB4DkP_ceYg6WQ-DBedjpqxd8J9veAVrLI62</recordid><startdate>20171101</startdate><enddate>20171101</enddate><creator>He, Jibo</creator><creator>Choi, William</creator><creator>Yang, Yan</creator><creator>Lu, Junshi</creator><creator>Wu, Xiaohui</creator><creator>Peng, Kaiping</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-5110-581X</orcidid></search><sort><creationdate>20171101</creationdate><title>Detection of driver drowsiness using wearable devices: A feasibility study of the proximity sensor</title><author>He, Jibo ; Choi, William ; Yang, Yan ; Lu, Junshi ; Wu, Xiaohui ; Peng, Kaiping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-7922a593def02fe5487e57bb91969f59eb12e17c9c347b0de12fd21242aace9a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adult</topic><topic>Algorithms</topic><topic>Attention</topic><topic>Automobile Driving - psychology</topic><topic>Biosensing Techniques - instrumentation</topic><topic>Biosensing Techniques - methods</topic><topic>Blinking</topic><topic>Driver drowsiness</topic><topic>Eye Movement Measurements - instrumentation</topic><topic>Eye Movements</topic><topic>Feasibility Studies</topic><topic>Female</topic><topic>Humans</topic><topic>Male</topic><topic>Proximity sensor</topic><topic>Sleep Stages - physiology</topic><topic>Wearable device</topic><topic>Wearable Electronic Devices</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>He, Jibo</creatorcontrib><creatorcontrib>Choi, William</creatorcontrib><creatorcontrib>Yang, Yan</creatorcontrib><creatorcontrib>Lu, Junshi</creatorcontrib><creatorcontrib>Wu, Xiaohui</creatorcontrib><creatorcontrib>Peng, Kaiping</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Applied ergonomics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>He, Jibo</au><au>Choi, William</au><au>Yang, Yan</au><au>Lu, Junshi</au><au>Wu, Xiaohui</au><au>Peng, Kaiping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detection of driver drowsiness using wearable devices: A feasibility study of the proximity sensor</atitle><jtitle>Applied ergonomics</jtitle><addtitle>Appl Ergon</addtitle><date>2017-11-01</date><risdate>2017</risdate><volume>65</volume><spage>473</spage><epage>480</epage><pages>473-480</pages><issn>0003-6870</issn><eissn>1872-9126</eissn><abstract>Drowsiness is one of the major factors that cause crashes in the transportation industry. Drowsiness detection systems can alert drowsy operators and potentially reduce the risk of crashes. In this study, a Google-Glass-based drowsiness detection system was developed and validated.
The proximity sensor of Google Glass was used to monitor eye blink frequency. A simulated driving study was carried out to validate the system. Driving performance and eye blinks were compared between the two states of alertness and drowsiness while driving.
Drowsy drivers increased frequency of eye blinks, produced longer braking response time and increased lane deviation, compared to when they were alert. A threshold algorithm for proximity sensor can reliably detect eye blinks and proved the feasibility of using Google Glass to detect operator drowsiness.
This technology provides a new platform to detect operator drowsiness and has the potential to reduce drowsiness-related crashes in driving and aviation.
•The infrared proximity sensor can monitor eye blinks.•The thresholding algorithm can use proximity value to detect eye blinks.•Prolonged driving increases eye blink frequency.•Longtime driving impairs driving performance.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>28420482</pmid><doi>10.1016/j.apergo.2017.02.016</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-5110-581X</orcidid></addata></record> |
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| ispartof | Applied ergonomics, 2017-11, Vol.65, p.473-480 |
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| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Adult Algorithms Attention Automobile Driving - psychology Biosensing Techniques - instrumentation Biosensing Techniques - methods Blinking Driver drowsiness Eye Movement Measurements - instrumentation Eye Movements Feasibility Studies Female Humans Male Proximity sensor Sleep Stages - physiology Wearable device Wearable Electronic Devices Young Adult |
| title | Detection of driver drowsiness using wearable devices: A feasibility study of the proximity sensor |
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