Analysis of body pressure distribution on car seats by using deep learning
This study aimed to extract information from body pressure distribution, including comfort, participant body size, and seat characteristics by using supervised deep learning, and body pressure characteristics corresponding to sensory evaluation by using unsupervised deep learning. Body pressure data...
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| Veröffentlicht in: | Applied ergonomics 2019-02, Vol.75, p.283-287 |
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| container_title | Applied ergonomics |
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| creator | Mitsuya, Reiko Kato, Kazuhito Kou, Nei Nakamura, Takeshi Sugawara, Kohei Dobashi, Hiroki Sugita, Takuro Kawai, Takashi |
| description | This study aimed to extract information from body pressure distribution, including comfort, participant body size, and seat characteristics by using supervised deep learning, and body pressure characteristics corresponding to sensory evaluation by using unsupervised deep learning. Body pressure data of 18 participants and 19 kinds of car seats were used for the analysis. Sensory evaluation of 9 items concerning cushion characteristics and seat comfort was conducted. From the analysis, we determined that body size and car seats could be classified with high precision by using body pressure distribution data. For the sensory evaluation items, the correct answer rate was high. By examining the importance of the cells of the mat, the features of the body pressure mat at the seat cushion and backrest, body size, car seat, and parts related to sensory evaluation could be determined in detail. The study findings can be applied in the development of car seats.
•Information from body pressure data by using supervised deep learning is extracted.•Body pressure characteristics by using unsupervised deep learning is found.•Body size and car seats could be classified with high precision.•Findings can be applied in the development of car seats. |
| doi_str_mv | 10.1016/j.apergo.2018.08.023 |
| format | Article |
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•Information from body pressure data by using supervised deep learning is extracted.•Body pressure characteristics by using unsupervised deep learning is found.•Body size and car seats could be classified with high precision.•Findings can be applied in the development of car seats.</description><identifier>ISSN: 0003-6870</identifier><identifier>EISSN: 1872-9126</identifier><identifier>DOI: 10.1016/j.apergo.2018.08.023</identifier><identifier>PMID: 30509538</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Adult ; Automobiles ; Body pressure distribution ; Body Size ; Car seat ; Characteristics extraction ; Deep Learning ; Equipment Design ; Ergonomics - methods ; Female ; Healthy Volunteers ; Humans ; Machine learning ; Male ; Pressure ; Sitting Position ; Support vector machine</subject><ispartof>Applied ergonomics, 2019-02, Vol.75, p.283-287</ispartof><rights>2018 Elsevier Ltd</rights><rights>Copyright © 2018 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c405t-69fb27edb89e6e1aac3f821236a0eb8db4835e08ac0a70cf102c074bc028fba13</citedby><cites>FETCH-LOGICAL-c405t-69fb27edb89e6e1aac3f821236a0eb8db4835e08ac0a70cf102c074bc028fba13</cites><orcidid>0000-0003-2608-927X ; 0000-0002-7174-4205 ; 0000-0003-3786-1771</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0003687018303351$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30509538$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mitsuya, Reiko</creatorcontrib><creatorcontrib>Kato, Kazuhito</creatorcontrib><creatorcontrib>Kou, Nei</creatorcontrib><creatorcontrib>Nakamura, Takeshi</creatorcontrib><creatorcontrib>Sugawara, Kohei</creatorcontrib><creatorcontrib>Dobashi, Hiroki</creatorcontrib><creatorcontrib>Sugita, Takuro</creatorcontrib><creatorcontrib>Kawai, Takashi</creatorcontrib><title>Analysis of body pressure distribution on car seats by using deep learning</title><title>Applied ergonomics</title><addtitle>Appl Ergon</addtitle><description>This study aimed to extract information from body pressure distribution, including comfort, participant body size, and seat characteristics by using supervised deep learning, and body pressure characteristics corresponding to sensory evaluation by using unsupervised deep learning. Body pressure data of 18 participants and 19 kinds of car seats were used for the analysis. Sensory evaluation of 9 items concerning cushion characteristics and seat comfort was conducted. From the analysis, we determined that body size and car seats could be classified with high precision by using body pressure distribution data. For the sensory evaluation items, the correct answer rate was high. By examining the importance of the cells of the mat, the features of the body pressure mat at the seat cushion and backrest, body size, car seat, and parts related to sensory evaluation could be determined in detail. The study findings can be applied in the development of car seats.
•Information from body pressure data by using supervised deep learning is extracted.•Body pressure characteristics by using unsupervised deep learning is found.•Body size and car seats could be classified with high precision.•Findings can be applied in the development of car seats.</description><subject>Adult</subject><subject>Automobiles</subject><subject>Body pressure distribution</subject><subject>Body Size</subject><subject>Car seat</subject><subject>Characteristics extraction</subject><subject>Deep Learning</subject><subject>Equipment Design</subject><subject>Ergonomics - methods</subject><subject>Female</subject><subject>Healthy Volunteers</subject><subject>Humans</subject><subject>Machine learning</subject><subject>Male</subject><subject>Pressure</subject><subject>Sitting Position</subject><subject>Support vector machine</subject><issn>0003-6870</issn><issn>1872-9126</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kFtLxDAQhYMoul7-gUgefek6SXpJXwQRrwi-6HNI0qlk6bY10wr7782y6qNwYDhwzgzzMXYuYClAlFerpR0xfgxLCUIvIUmqPbYQupJZLWS5zxYAoLJSV3DEjolWyepcFIfsSEEBdaH0gj3f9LbbUCA-tNwNzYaPEYnmiLwJNMXg5ikMPU_yNnJCOxF3Gz5T6D94gzjyDm3skztlB63tCM9-5gl7v797u33MXl4fnm5vXjKfQzFlZd06WWHjdI0lCmu9arUUUpUW0OnG5VoVCNp6sBX4VoD0UOXOg9Sts0KdsMvd3jEOnzPSZNaBPHad7XGYyUiR17ooqqpK0XwX9XEgitiaMYa1jRsjwGwpmpXZUTRbigaSpEq1i58Ls1tj81f6xZYC17sApj-_AkZDPmDvsQkR_WSaIfx_4RtYI4XF</recordid><startdate>20190201</startdate><enddate>20190201</enddate><creator>Mitsuya, Reiko</creator><creator>Kato, Kazuhito</creator><creator>Kou, Nei</creator><creator>Nakamura, Takeshi</creator><creator>Sugawara, Kohei</creator><creator>Dobashi, Hiroki</creator><creator>Sugita, Takuro</creator><creator>Kawai, Takashi</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/0000-0003-2608-927X</orcidid><orcidid>https://orcid.org/0000-0002-7174-4205</orcidid><orcidid>https://orcid.org/0000-0003-3786-1771</orcidid></search><sort><creationdate>20190201</creationdate><title>Analysis of body pressure distribution on car seats by using deep learning</title><author>Mitsuya, Reiko ; Kato, Kazuhito ; Kou, Nei ; Nakamura, Takeshi ; Sugawara, Kohei ; Dobashi, Hiroki ; Sugita, Takuro ; Kawai, Takashi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-69fb27edb89e6e1aac3f821236a0eb8db4835e08ac0a70cf102c074bc028fba13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>Automobiles</topic><topic>Body pressure distribution</topic><topic>Body Size</topic><topic>Car seat</topic><topic>Characteristics extraction</topic><topic>Deep Learning</topic><topic>Equipment Design</topic><topic>Ergonomics - methods</topic><topic>Female</topic><topic>Healthy Volunteers</topic><topic>Humans</topic><topic>Machine learning</topic><topic>Male</topic><topic>Pressure</topic><topic>Sitting Position</topic><topic>Support vector machine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mitsuya, Reiko</creatorcontrib><creatorcontrib>Kato, Kazuhito</creatorcontrib><creatorcontrib>Kou, Nei</creatorcontrib><creatorcontrib>Nakamura, Takeshi</creatorcontrib><creatorcontrib>Sugawara, Kohei</creatorcontrib><creatorcontrib>Dobashi, Hiroki</creatorcontrib><creatorcontrib>Sugita, Takuro</creatorcontrib><creatorcontrib>Kawai, Takashi</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>Mitsuya, Reiko</au><au>Kato, Kazuhito</au><au>Kou, Nei</au><au>Nakamura, Takeshi</au><au>Sugawara, Kohei</au><au>Dobashi, Hiroki</au><au>Sugita, Takuro</au><au>Kawai, Takashi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of body pressure distribution on car seats by using deep learning</atitle><jtitle>Applied ergonomics</jtitle><addtitle>Appl Ergon</addtitle><date>2019-02-01</date><risdate>2019</risdate><volume>75</volume><spage>283</spage><epage>287</epage><pages>283-287</pages><issn>0003-6870</issn><eissn>1872-9126</eissn><abstract>This study aimed to extract information from body pressure distribution, including comfort, participant body size, and seat characteristics by using supervised deep learning, and body pressure characteristics corresponding to sensory evaluation by using unsupervised deep learning. Body pressure data of 18 participants and 19 kinds of car seats were used for the analysis. Sensory evaluation of 9 items concerning cushion characteristics and seat comfort was conducted. From the analysis, we determined that body size and car seats could be classified with high precision by using body pressure distribution data. For the sensory evaluation items, the correct answer rate was high. By examining the importance of the cells of the mat, the features of the body pressure mat at the seat cushion and backrest, body size, car seat, and parts related to sensory evaluation could be determined in detail. The study findings can be applied in the development of car seats.
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| source | MEDLINE; Elsevier ScienceDirect Journals Complete |
| subjects | Adult Automobiles Body pressure distribution Body Size Car seat Characteristics extraction Deep Learning Equipment Design Ergonomics - methods Female Healthy Volunteers Humans Machine learning Male Pressure Sitting Position Support vector machine |
| title | Analysis of body pressure distribution on car seats by using deep learning |
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