Gesture Recognition–Based Smart Training Assistant System for Construction Worker Earplug-Wearing Training
AbstractThousands of construction workers suffer noise-induced hearing loss (NIHL) every year from excessive noise exposure on the job, which impairs the quality of their lives and increases the risk of injury. Properly wearing earplugs is very important onsite for worker hearing protection. However...
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Veröffentlicht in: | Journal of construction engineering and management 2020-12, Vol.146 (12) |
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creator | Bangaru, Srikanth Sagar Wang, Chao Zhou, Xu Jeon, Hyun Woo Li, Yulong |
description | AbstractThousands of construction workers suffer noise-induced hearing loss (NIHL) every year from excessive noise exposure on the job, which impairs the quality of their lives and increases the risk of injury. Properly wearing earplugs is very important onsite for worker hearing protection. However, the training provided in the current practice is minimal. Therefore, there is a need to develop an efficient and effective self-training method that can provide both accurate step-by-step earplug-wearing instructions and timely feedback through monitoring. With the development of artificial intelligence and wearable sensor technologies, the possibility of developing an advanced intelligent training method becomes plausible. Therefore, the objective of this paper is to develop a gesture recognition–based smart training assistant system that can automatically evaluate workers’ performance during their earplug-wearing self-training and provide timely feedback to rectify any mistakes. Through the system feasibility test and performance evaluation, the results show that the proposed system can achieve around 90% training accuracy and around 80% testing accuracy recognizing the classified forearm gestures of wearing earplugs for noise protection training using the developed artificial neural network (ANN) models for both hands. The proposed gesture recognition–based smart training assistant system will eventually help industries to improve the performance and safety of employees with low implementation costs. |
doi_str_mv | 10.1061/(ASCE)CO.1943-7862.0001941 |
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Properly wearing earplugs is very important onsite for worker hearing protection. However, the training provided in the current practice is minimal. Therefore, there is a need to develop an efficient and effective self-training method that can provide both accurate step-by-step earplug-wearing instructions and timely feedback through monitoring. With the development of artificial intelligence and wearable sensor technologies, the possibility of developing an advanced intelligent training method becomes plausible. Therefore, the objective of this paper is to develop a gesture recognition–based smart training assistant system that can automatically evaluate workers’ performance during their earplug-wearing self-training and provide timely feedback to rectify any mistakes. Through the system feasibility test and performance evaluation, the results show that the proposed system can achieve around 90% training accuracy and around 80% testing accuracy recognizing the classified forearm gestures of wearing earplugs for noise protection training using the developed artificial neural network (ANN) models for both hands. The proposed gesture recognition–based smart training assistant system will eventually help industries to improve the performance and safety of employees with low implementation costs.</description><identifier>ISSN: 0733-9364</identifier><identifier>EISSN: 1943-7862</identifier><identifier>DOI: 10.1061/(ASCE)CO.1943-7862.0001941</identifier><language>eng</language><publisher>New York: American Society of Civil Engineers</publisher><subject>Artificial intelligence ; Artificial neural networks ; Construction industry ; Ear protection ; Feedback ; Forearm ; Gesture recognition ; Hearing protection ; Hearing protectors ; Noise ; Noise levels ; Performance enhancement ; Performance evaluation ; Technical Papers ; Training</subject><ispartof>Journal of construction engineering and management, 2020-12, Vol.146 (12)</ispartof><rights>2020 American Society of Civil Engineers</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a395t-89bbab6fff875126f3c1dd6a79e5669e6010c79562d57621e4351ddc7d9134703</citedby><cites>FETCH-LOGICAL-a395t-89bbab6fff875126f3c1dd6a79e5669e6010c79562d57621e4351ddc7d9134703</cites><orcidid>0000-0002-5085-2522 ; 0000-0003-2221-7959 ; 0000-0002-9322-7778</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttp://ascelibrary.org/doi/pdf/10.1061/(ASCE)CO.1943-7862.0001941$$EPDF$$P50$$Gasce$$H</linktopdf><linktohtml>$$Uhttp://ascelibrary.org/doi/abs/10.1061/(ASCE)CO.1943-7862.0001941$$EHTML$$P50$$Gasce$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,76193,76201</link.rule.ids></links><search><creatorcontrib>Bangaru, Srikanth Sagar</creatorcontrib><creatorcontrib>Wang, Chao</creatorcontrib><creatorcontrib>Zhou, Xu</creatorcontrib><creatorcontrib>Jeon, Hyun Woo</creatorcontrib><creatorcontrib>Li, Yulong</creatorcontrib><title>Gesture Recognition–Based Smart Training Assistant System for Construction Worker Earplug-Wearing Training</title><title>Journal of construction engineering and management</title><description>AbstractThousands of construction workers suffer noise-induced hearing loss (NIHL) every year from excessive noise exposure on the job, which impairs the quality of their lives and increases the risk of injury. Properly wearing earplugs is very important onsite for worker hearing protection. However, the training provided in the current practice is minimal. Therefore, there is a need to develop an efficient and effective self-training method that can provide both accurate step-by-step earplug-wearing instructions and timely feedback through monitoring. With the development of artificial intelligence and wearable sensor technologies, the possibility of developing an advanced intelligent training method becomes plausible. Therefore, the objective of this paper is to develop a gesture recognition–based smart training assistant system that can automatically evaluate workers’ performance during their earplug-wearing self-training and provide timely feedback to rectify any mistakes. Through the system feasibility test and performance evaluation, the results show that the proposed system can achieve around 90% training accuracy and around 80% testing accuracy recognizing the classified forearm gestures of wearing earplugs for noise protection training using the developed artificial neural network (ANN) models for both hands. The proposed gesture recognition–based smart training assistant system will eventually help industries to improve the performance and safety of employees with low implementation costs.</description><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Construction industry</subject><subject>Ear protection</subject><subject>Feedback</subject><subject>Forearm</subject><subject>Gesture recognition</subject><subject>Hearing protection</subject><subject>Hearing protectors</subject><subject>Noise</subject><subject>Noise levels</subject><subject>Performance enhancement</subject><subject>Performance evaluation</subject><subject>Technical Papers</subject><subject>Training</subject><issn>0733-9364</issn><issn>1943-7862</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1kMFOAjEURRujiYj-Q6MbXQy202lL3eEE0YSERDAsm9LpkEGYYttZsPMf_EO_xE4AXblqXnPPfXkHgGuMehgxfH87mObDu3zSwyIjCe-ztIcQigM-AZ3fv1PQQZyQRBCWnYML71cxkzFBO2A9Mj40zsBXo-2yrkJl6-_Pr0flTQGnG-UCnDlV1VW9hAPvKx9UHeB054PZwNI6mNvaB9foFoRz696Ng0PltutmmcyNci14bLgEZ6Vae3N1eLvg7Wk4y5-T8WT0kg_GiSKChqQvFgu1YGVZ9jnFKSuJxkXBFBeGMiYMQxhpLihLC8pZik1GaAxoXghMMo5IF9zse7fOfjTxQLmyjavjSplmFEUXlIuYetintLPeO1PKravixTuJkWztStnalflEtiZla1Ie7EaY7WHltfmrP5L_gz-ge4CD</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Bangaru, Srikanth Sagar</creator><creator>Wang, Chao</creator><creator>Zhou, Xu</creator><creator>Jeon, Hyun Woo</creator><creator>Li, Yulong</creator><general>American Society of Civil Engineers</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><orcidid>https://orcid.org/0000-0002-5085-2522</orcidid><orcidid>https://orcid.org/0000-0003-2221-7959</orcidid><orcidid>https://orcid.org/0000-0002-9322-7778</orcidid></search><sort><creationdate>20201201</creationdate><title>Gesture Recognition–Based Smart Training Assistant System for Construction Worker Earplug-Wearing Training</title><author>Bangaru, Srikanth Sagar ; Wang, Chao ; Zhou, Xu ; Jeon, Hyun Woo ; Li, Yulong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a395t-89bbab6fff875126f3c1dd6a79e5669e6010c79562d57621e4351ddc7d9134703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Construction industry</topic><topic>Ear protection</topic><topic>Feedback</topic><topic>Forearm</topic><topic>Gesture recognition</topic><topic>Hearing protection</topic><topic>Hearing protectors</topic><topic>Noise</topic><topic>Noise levels</topic><topic>Performance enhancement</topic><topic>Performance evaluation</topic><topic>Technical Papers</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bangaru, Srikanth Sagar</creatorcontrib><creatorcontrib>Wang, Chao</creatorcontrib><creatorcontrib>Zhou, Xu</creatorcontrib><creatorcontrib>Jeon, Hyun Woo</creatorcontrib><creatorcontrib>Li, Yulong</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Journal of construction engineering and management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bangaru, Srikanth Sagar</au><au>Wang, Chao</au><au>Zhou, Xu</au><au>Jeon, Hyun Woo</au><au>Li, Yulong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gesture Recognition–Based Smart Training Assistant System for Construction Worker Earplug-Wearing Training</atitle><jtitle>Journal of construction engineering and management</jtitle><date>2020-12-01</date><risdate>2020</risdate><volume>146</volume><issue>12</issue><issn>0733-9364</issn><eissn>1943-7862</eissn><abstract>AbstractThousands of construction workers suffer noise-induced hearing loss (NIHL) every year from excessive noise exposure on the job, which impairs the quality of their lives and increases the risk of injury. Properly wearing earplugs is very important onsite for worker hearing protection. However, the training provided in the current practice is minimal. Therefore, there is a need to develop an efficient and effective self-training method that can provide both accurate step-by-step earplug-wearing instructions and timely feedback through monitoring. With the development of artificial intelligence and wearable sensor technologies, the possibility of developing an advanced intelligent training method becomes plausible. Therefore, the objective of this paper is to develop a gesture recognition–based smart training assistant system that can automatically evaluate workers’ performance during their earplug-wearing self-training and provide timely feedback to rectify any mistakes. Through the system feasibility test and performance evaluation, the results show that the proposed system can achieve around 90% training accuracy and around 80% testing accuracy recognizing the classified forearm gestures of wearing earplugs for noise protection training using the developed artificial neural network (ANN) models for both hands. The proposed gesture recognition–based smart training assistant system will eventually help industries to improve the performance and safety of employees with low implementation costs.</abstract><cop>New York</cop><pub>American Society of Civil Engineers</pub><doi>10.1061/(ASCE)CO.1943-7862.0001941</doi><orcidid>https://orcid.org/0000-0002-5085-2522</orcidid><orcidid>https://orcid.org/0000-0003-2221-7959</orcidid><orcidid>https://orcid.org/0000-0002-9322-7778</orcidid></addata></record> |
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subjects | Artificial intelligence Artificial neural networks Construction industry Ear protection Feedback Forearm Gesture recognition Hearing protection Hearing protectors Noise Noise levels Performance enhancement Performance evaluation Technical Papers Training |
title | Gesture Recognition–Based Smart Training Assistant System for Construction Worker Earplug-Wearing Training |
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