Monitoring distraction of construction workers caused by noise using a wearable Electroencephalography (EEG) device
In the construction environment with high attention requirements, distraction is the main cause of unsafe behavior and safety performance degradation. However, few studies have focused on distraction's cognitive features and how to monitor it objectively in the construction workplace. To fill t...
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description | In the construction environment with high attention requirements, distraction is the main cause of unsafe behavior and safety performance degradation. However, few studies have focused on distraction's cognitive features and how to monitor it objectively in the construction workplace. To fill the research gap, the present study examined the correlation between distraction and brain activity using an Electroencephalography (EEG) device, intending to provide an approach for objectively monitoring worker distraction. In the simulated hazards identification activity, sustained attention to response task and dual-task paradigms have been employed to induce distraction combined with noise interference. Twenty-seven subjects participated in the experiment to identify whether a hazardous opening exists or not in the workplace in the shown images. The EEG waves were recorded and divided into two groups according to task performance: focused and distracted. Through feature calculation and extraction, it was found that beta and gamma powers in the left temporal and right pre-frontal cortex can distinguish these two statuses, particularly in channels T7 and AF4. The indicators can be considered as an objective evaluation of an individual's sustained attention and attention failures. The developed indicators located in specified brain zones can also be used as a reference for attention training. By providing safety managers with attention status about the workers in high-risk workplaces, distraction detection contributes to control and regulate work error and improper operation, which can extend to apply in other attentive jobs like drivers, pilots, surgeons, and lifeguards.
•An objective distraction monitoring method is proposed.•EEG frequency features can distinguish focused and distracted state.•Feasibility of distraction monitoring method is validated by SVM classification.•Classification performance of channel T7 and AF4 outperformed other electrodes. |
doi_str_mv | 10.1016/j.autcon.2021.103598 |
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•An objective distraction monitoring method is proposed.•EEG frequency features can distinguish focused and distracted state.•Feasibility of distraction monitoring method is validated by SVM classification.•Classification performance of channel T7 and AF4 outperformed other electrodes.</description><identifier>ISSN: 0926-5805</identifier><identifier>EISSN: 1872-7891</identifier><identifier>DOI: 10.1016/j.autcon.2021.103598</identifier><language>eng</language><publisher>AMSTERDAM: Elsevier B.V</publisher><subject>Brain ; Construction & Building Technology ; Construction industry ; EEG ; Electroencephalography ; Engineering ; Engineering, Civil ; Feature extraction ; Hazard identification ; Hazards identification ; Indicators ; Monitoring ; Noise-induced distraction ; Performance degradation ; Safety management ; Science & Technology ; Sustained attention ; Technology ; Workplaces</subject><ispartof>Automation in construction, 2021-05, Vol.125, p.103598, Article 103598</ispartof><rights>2021 Elsevier B.V.</rights><rights>Copyright Elsevier BV May 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>53</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000649681000003</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c400t-463e0699b0eb01f8f1044206616035c7ba7a42097a06ca4af0e1220f235910113</citedby><cites>FETCH-LOGICAL-c400t-463e0699b0eb01f8f1044206616035c7ba7a42097a06ca4af0e1220f235910113</cites><orcidid>0000-0003-3082-4951 ; 0000-0001-9396-0059</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.autcon.2021.103598$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,782,786,3554,27933,27934,39267,46004</link.rule.ids></links><search><creatorcontrib>Ke, Jinjing</creatorcontrib><creatorcontrib>Zhang, Ming</creatorcontrib><creatorcontrib>Luo, Xiaowei</creatorcontrib><creatorcontrib>Chen, Jiayu</creatorcontrib><title>Monitoring distraction of construction workers caused by noise using a wearable Electroencephalography (EEG) device</title><title>Automation in construction</title><addtitle>AUTOMAT CONSTR</addtitle><description>In the construction environment with high attention requirements, distraction is the main cause of unsafe behavior and safety performance degradation. However, few studies have focused on distraction's cognitive features and how to monitor it objectively in the construction workplace. To fill the research gap, the present study examined the correlation between distraction and brain activity using an Electroencephalography (EEG) device, intending to provide an approach for objectively monitoring worker distraction. In the simulated hazards identification activity, sustained attention to response task and dual-task paradigms have been employed to induce distraction combined with noise interference. Twenty-seven subjects participated in the experiment to identify whether a hazardous opening exists or not in the workplace in the shown images. The EEG waves were recorded and divided into two groups according to task performance: focused and distracted. Through feature calculation and extraction, it was found that beta and gamma powers in the left temporal and right pre-frontal cortex can distinguish these two statuses, particularly in channels T7 and AF4. The indicators can be considered as an objective evaluation of an individual's sustained attention and attention failures. The developed indicators located in specified brain zones can also be used as a reference for attention training. By providing safety managers with attention status about the workers in high-risk workplaces, distraction detection contributes to control and regulate work error and improper operation, which can extend to apply in other attentive jobs like drivers, pilots, surgeons, and lifeguards.
•An objective distraction monitoring method is proposed.•EEG frequency features can distinguish focused and distracted state.•Feasibility of distraction monitoring method is validated by SVM classification.•Classification performance of channel T7 and AF4 outperformed other electrodes.</description><subject>Brain</subject><subject>Construction & Building Technology</subject><subject>Construction industry</subject><subject>EEG</subject><subject>Electroencephalography</subject><subject>Engineering</subject><subject>Engineering, Civil</subject><subject>Feature extraction</subject><subject>Hazard identification</subject><subject>Hazards identification</subject><subject>Indicators</subject><subject>Monitoring</subject><subject>Noise-induced distraction</subject><subject>Performance degradation</subject><subject>Safety management</subject><subject>Science & Technology</subject><subject>Sustained attention</subject><subject>Technology</subject><subject>Workplaces</subject><issn>0926-5805</issn><issn>1872-7891</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><recordid>eNqNkM9LwzAUx4MoOKf_gYeAF0U6X9IubS-CjPkDFC96Dmn6umXOZibpxv57UyoexVzCC-_z8r4fQs4ZTBgwcbOaqC5o2044cBaf0mlZHJARK3Ke5EXJDskISi6SaQHTY3Li_QoAchDliPgX25pgnWkXtDY-OKWDsS21DY0DY90N9c66D3SeatV5rGm1p601Hmnne1LRHSqnqjXS-Rp1cBZbjZulWtuFU5vlnl7O5w9XtMat0XhKjhq19nj2c4_J-_38bfaYPL8-PM3unhOdAYQkEynGHcsKsALWFA2DLOMgBBMxoc4rlatYl7kCoVWmGkDGOTQ8xo9aWDomF8PcjbNfHfogV7ZzbfxS8mnKWSmyOGlMsqFLO-u9w0ZunPlUbi8ZyF6vXMlBr-z1ykFvxIoB22FlG69Nn_gXjX5FVoqCQX_SmQmq1zizXRsiev1_NHbfDt0YVW0NOvlD1MZF1bK25u9NvwF8IqYb</recordid><startdate>202105</startdate><enddate>202105</enddate><creator>Ke, Jinjing</creator><creator>Zhang, Ming</creator><creator>Luo, Xiaowei</creator><creator>Chen, Jiayu</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier BV</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-3082-4951</orcidid><orcidid>https://orcid.org/0000-0001-9396-0059</orcidid></search><sort><creationdate>202105</creationdate><title>Monitoring distraction of construction workers caused by noise using a wearable Electroencephalography (EEG) device</title><author>Ke, Jinjing ; Zhang, Ming ; Luo, Xiaowei ; Chen, Jiayu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c400t-463e0699b0eb01f8f1044206616035c7ba7a42097a06ca4af0e1220f235910113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Brain</topic><topic>Construction & Building Technology</topic><topic>Construction industry</topic><topic>EEG</topic><topic>Electroencephalography</topic><topic>Engineering</topic><topic>Engineering, Civil</topic><topic>Feature extraction</topic><topic>Hazard identification</topic><topic>Hazards identification</topic><topic>Indicators</topic><topic>Monitoring</topic><topic>Noise-induced distraction</topic><topic>Performance degradation</topic><topic>Safety management</topic><topic>Science & Technology</topic><topic>Sustained attention</topic><topic>Technology</topic><topic>Workplaces</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ke, Jinjing</creatorcontrib><creatorcontrib>Zhang, Ming</creatorcontrib><creatorcontrib>Luo, Xiaowei</creatorcontrib><creatorcontrib>Chen, Jiayu</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Automation in construction</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ke, Jinjing</au><au>Zhang, Ming</au><au>Luo, Xiaowei</au><au>Chen, Jiayu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Monitoring distraction of construction workers caused by noise using a wearable Electroencephalography (EEG) device</atitle><jtitle>Automation in construction</jtitle><stitle>AUTOMAT CONSTR</stitle><date>2021-05</date><risdate>2021</risdate><volume>125</volume><spage>103598</spage><pages>103598-</pages><artnum>103598</artnum><issn>0926-5805</issn><eissn>1872-7891</eissn><abstract>In the construction environment with high attention requirements, distraction is the main cause of unsafe behavior and safety performance degradation. However, few studies have focused on distraction's cognitive features and how to monitor it objectively in the construction workplace. To fill the research gap, the present study examined the correlation between distraction and brain activity using an Electroencephalography (EEG) device, intending to provide an approach for objectively monitoring worker distraction. In the simulated hazards identification activity, sustained attention to response task and dual-task paradigms have been employed to induce distraction combined with noise interference. Twenty-seven subjects participated in the experiment to identify whether a hazardous opening exists or not in the workplace in the shown images. The EEG waves were recorded and divided into two groups according to task performance: focused and distracted. Through feature calculation and extraction, it was found that beta and gamma powers in the left temporal and right pre-frontal cortex can distinguish these two statuses, particularly in channels T7 and AF4. The indicators can be considered as an objective evaluation of an individual's sustained attention and attention failures. The developed indicators located in specified brain zones can also be used as a reference for attention training. By providing safety managers with attention status about the workers in high-risk workplaces, distraction detection contributes to control and regulate work error and improper operation, which can extend to apply in other attentive jobs like drivers, pilots, surgeons, and lifeguards.
•An objective distraction monitoring method is proposed.•EEG frequency features can distinguish focused and distracted state.•Feasibility of distraction monitoring method is validated by SVM classification.•Classification performance of channel T7 and AF4 outperformed other electrodes.</abstract><cop>AMSTERDAM</cop><pub>Elsevier B.V</pub><doi>10.1016/j.autcon.2021.103598</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0003-3082-4951</orcidid><orcidid>https://orcid.org/0000-0001-9396-0059</orcidid></addata></record> |
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subjects | Brain Construction & Building Technology Construction industry EEG Electroencephalography Engineering Engineering, Civil Feature extraction Hazard identification Hazards identification Indicators Monitoring Noise-induced distraction Performance degradation Safety management Science & Technology Sustained attention Technology Workplaces |
title | Monitoring distraction of construction workers caused by noise using a wearable Electroencephalography (EEG) device |
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