Gesture recognition device based on cross reticulated graphene strain sensors
Perception and recognition of hand gestures have received much attention for their applications in the human–computer fields. The data-glove application in gesture recognition makes it expediently to track the movements of human fingers. Whereas, the development of the data-glove for hand gesture st...
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| Veröffentlicht in: | Journal of materials science. Materials in electronics 2021-04, Vol.32 (7), p.8410-8417 |
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| container_title | Journal of materials science. Materials in electronics |
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| creator | Yuan, Linlin Qi, Weiye Cai, Kaiyu Li, Chunhua Qian, Qiuping Zhou, Yunlong |
| description | Perception and recognition of hand gestures have received much attention for their applications in the human–computer fields. The data-glove application in gesture recognition makes it expediently to track the movements of human fingers. Whereas, the development of the data-glove for hand gesture still face numerous technical difficulties, such as the sensitivity, flexible ability, and system integration under low-cost. In this paper, a simple gesture recognition glove system was developed to characterize the sign language, which consists of cross reticulated graphene (CRG) flexible sensors, a multi-channel data acquisition module, and an artificial neural network algorithm. The designed acquisition module has several channels, so that signs from five sensors can be collected and transferred to the intelligent terminal to execute the hand gesture recognition algorithm. Besides, the recognition results suggest that the recognition rate of the neural network for 22 letters in the English alphabet can reach more than 90% and the overall recognition has an over 86% accuracy. |
| doi_str_mv | 10.1007/s10854-021-05448-x |
| format | Article |
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The data-glove application in gesture recognition makes it expediently to track the movements of human fingers. Whereas, the development of the data-glove for hand gesture still face numerous technical difficulties, such as the sensitivity, flexible ability, and system integration under low-cost. In this paper, a simple gesture recognition glove system was developed to characterize the sign language, which consists of cross reticulated graphene (CRG) flexible sensors, a multi-channel data acquisition module, and an artificial neural network algorithm. The designed acquisition module has several channels, so that signs from five sensors can be collected and transferred to the intelligent terminal to execute the hand gesture recognition algorithm. 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Besides, the recognition results suggest that the recognition rate of the neural network for 22 letters in the English alphabet can reach more than 90% and the overall recognition has an over 86% accuracy.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Characterization and Evaluation of Materials</subject><subject>Chemistry and Materials Science</subject><subject>Flexible components</subject><subject>Gesture recognition</subject><subject>Graphene</subject><subject>Materials Science</subject><subject>Modules</subject><subject>Neural networks</subject><subject>Optical and Electronic Materials</subject><subject>Sensors</subject><issn>0957-4522</issn><issn>1573-482X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9kE1LAzEQhoMoWD_-gKcFz9GZfG1ylKJVqHhR8Ba22WndUndrsiv13xu7gjdPwwzP-87My9gFwhUClNcJwWrFQSAHrZTluwM2QV1Krqx4PWQTcLrkSgtxzE5SWgOAUdJO2OOMUj9EKiKFbtU2fdO1RU2fTaBiUSWqi9yH2KWUib4Jw6bq83AVq-0btVSkPlZNWyRqUxfTGTtaVptE57_1lL3c3T5P7_n8afYwvZnzINH13JC00mgAZ6VbBKElBOscBhRKoCqNDJYCuQqAjAq1qdGEYI1EXDpclPKUXY6-29h9DPkDv-6G2OaVXmjUymqDJlNipPb3R1r6bWzeq_jlEfxPbH6MzefY_D42v8siOYpShtsVxT_rf1TfFcxwYA</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Yuan, Linlin</creator><creator>Qi, Weiye</creator><creator>Cai, Kaiyu</creator><creator>Li, Chunhua</creator><creator>Qian, Qiuping</creator><creator>Zhou, Yunlong</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>S0W</scope><orcidid>https://orcid.org/0000-0001-5654-1170</orcidid></search><sort><creationdate>20210401</creationdate><title>Gesture recognition device based on cross reticulated graphene strain sensors</title><author>Yuan, Linlin ; Qi, Weiye ; Cai, Kaiyu ; Li, Chunhua ; Qian, Qiuping ; Zhou, Yunlong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-6e38365009839bc2530c8991c124214763c8ece9a00e64cd6d16cc86311f91b73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Characterization and Evaluation of Materials</topic><topic>Chemistry and Materials Science</topic><topic>Flexible components</topic><topic>Gesture recognition</topic><topic>Graphene</topic><topic>Materials Science</topic><topic>Modules</topic><topic>Neural networks</topic><topic>Optical and Electronic Materials</topic><topic>Sensors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yuan, Linlin</creatorcontrib><creatorcontrib>Qi, Weiye</creatorcontrib><creatorcontrib>Cai, Kaiyu</creatorcontrib><creatorcontrib>Li, Chunhua</creatorcontrib><creatorcontrib>Qian, Qiuping</creatorcontrib><creatorcontrib>Zhou, Yunlong</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>DELNET Engineering & Technology Collection</collection><jtitle>Journal of materials science. Materials in electronics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yuan, Linlin</au><au>Qi, Weiye</au><au>Cai, Kaiyu</au><au>Li, Chunhua</au><au>Qian, Qiuping</au><au>Zhou, Yunlong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gesture recognition device based on cross reticulated graphene strain sensors</atitle><jtitle>Journal of materials science. Materials in electronics</jtitle><stitle>J Mater Sci: Mater Electron</stitle><date>2021-04-01</date><risdate>2021</risdate><volume>32</volume><issue>7</issue><spage>8410</spage><epage>8417</epage><pages>8410-8417</pages><issn>0957-4522</issn><eissn>1573-482X</eissn><abstract>Perception and recognition of hand gestures have received much attention for their applications in the human–computer fields. The data-glove application in gesture recognition makes it expediently to track the movements of human fingers. 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| subjects | Algorithms Artificial neural networks Characterization and Evaluation of Materials Chemistry and Materials Science Flexible components Gesture recognition Graphene Materials Science Modules Neural networks Optical and Electronic Materials Sensors |
| title | Gesture recognition device based on cross reticulated graphene strain sensors |
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