Stretchable e-Skin Patch for Gesture Recognition on the Back of the Hand
Gesture recognition is important for human-computer interaction and a variety of emerging research and commercial areas including virtual and augmented reality. Current approaches typically require sensors to be placed on the forearm, wrist, or directly across finger joints; however, they can be cum...
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Veröffentlicht in: | IEEE transactions on industrial electronics (1982) 2020-01, Vol.67 (1), p.647-657 |
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creator | Jiang, Shuo Li, Ling Xu, Haipeng Xu, Junkai Gu, Guoying Shull, Peter B. |
description | Gesture recognition is important for human-computer interaction and a variety of emerging research and commercial areas including virtual and augmented reality. Current approaches typically require sensors to be placed on the forearm, wrist, or directly across finger joints; however, they can be cumbersome or hinder human movement and sensation. In this paper, we introduce a novel approach to recognize hand gestures by estimating skin strain with multiple soft sensors optimally placed across the back of the hand. A pilot study was first conducted by covering the back of the hand with 40 small 2.5 mm reflective markers and using a high-precision camera system to measure skin strain patterns for individual finger movements. Optimal strain locations are then determined and used for sensor placement in a stretchable e-skin patch prototype. Experimental testing is performed to evaluate the stretchable e-skin patch performance in classifying individual finger gestures and American Sign Language 0-9 number gestures. Results showed classification accuracies of 95.3% and 94.4% for finger gestures and American Sign Language 0-9 gestures, respectively. These results demonstrate the feasibility of a stretchable e-skin patch on the back of the hand for hand gesture recognition and their potential to significantly enhance human-computer interaction. |
doi_str_mv | 10.1109/TIE.2019.2914621 |
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Current approaches typically require sensors to be placed on the forearm, wrist, or directly across finger joints; however, they can be cumbersome or hinder human movement and sensation. In this paper, we introduce a novel approach to recognize hand gestures by estimating skin strain with multiple soft sensors optimally placed across the back of the hand. A pilot study was first conducted by covering the back of the hand with 40 small 2.5 mm reflective markers and using a high-precision camera system to measure skin strain patterns for individual finger movements. Optimal strain locations are then determined and used for sensor placement in a stretchable e-skin patch prototype. Experimental testing is performed to evaluate the stretchable e-skin patch performance in classifying individual finger gestures and American Sign Language 0-9 number gestures. Results showed classification accuracies of 95.3% and 94.4% for finger gestures and American Sign Language 0-9 gestures, respectively. These results demonstrate the feasibility of a stretchable e-skin patch on the back of the hand for hand gesture recognition and their potential to significantly enhance human-computer interaction.</description><identifier>ISSN: 0278-0046</identifier><identifier>EISSN: 1557-9948</identifier><identifier>DOI: 10.1109/TIE.2019.2914621</identifier><identifier>CODEN: ITIED6</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Augmented reality ; Classification ; Feature selection ; Finger jointing ; Forearm ; Gesture recognition ; Human motion ; human–computer interaction ; Muscles ; Optimization ; Recognition ; Sensor phenomena and characterization ; Sensors ; Sign language ; Skin ; skin stretch ; soft sensing ; Strain ; Transdermal medication ; Virtual reality ; Wrist</subject><ispartof>IEEE transactions on industrial electronics (1982), 2020-01, Vol.67 (1), p.647-657</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-4c5ff5025c24ba4544e287fd3ebbc5ee4fad310a1f35842130cfbadc7fb227bd3</citedby><cites>FETCH-LOGICAL-c291t-4c5ff5025c24ba4544e287fd3ebbc5ee4fad310a1f35842130cfbadc7fb227bd3</cites><orcidid>0000-0002-7778-4523 ; 0000-0003-2770-7295 ; 0000-0001-8931-5743 ; 0000-0003-3645-6301 ; 0000-0002-3215-6136</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8709977$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8709977$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jiang, Shuo</creatorcontrib><creatorcontrib>Li, Ling</creatorcontrib><creatorcontrib>Xu, Haipeng</creatorcontrib><creatorcontrib>Xu, Junkai</creatorcontrib><creatorcontrib>Gu, Guoying</creatorcontrib><creatorcontrib>Shull, Peter B.</creatorcontrib><title>Stretchable e-Skin Patch for Gesture Recognition on the Back of the Hand</title><title>IEEE transactions on industrial electronics (1982)</title><addtitle>TIE</addtitle><description>Gesture recognition is important for human-computer interaction and a variety of emerging research and commercial areas including virtual and augmented reality. Current approaches typically require sensors to be placed on the forearm, wrist, or directly across finger joints; however, they can be cumbersome or hinder human movement and sensation. In this paper, we introduce a novel approach to recognize hand gestures by estimating skin strain with multiple soft sensors optimally placed across the back of the hand. A pilot study was first conducted by covering the back of the hand with 40 small 2.5 mm reflective markers and using a high-precision camera system to measure skin strain patterns for individual finger movements. Optimal strain locations are then determined and used for sensor placement in a stretchable e-skin patch prototype. Experimental testing is performed to evaluate the stretchable e-skin patch performance in classifying individual finger gestures and American Sign Language 0-9 number gestures. Results showed classification accuracies of 95.3% and 94.4% for finger gestures and American Sign Language 0-9 gestures, respectively. These results demonstrate the feasibility of a stretchable e-skin patch on the back of the hand for hand gesture recognition and their potential to significantly enhance human-computer interaction.</description><subject>Augmented reality</subject><subject>Classification</subject><subject>Feature selection</subject><subject>Finger jointing</subject><subject>Forearm</subject><subject>Gesture recognition</subject><subject>Human motion</subject><subject>human–computer interaction</subject><subject>Muscles</subject><subject>Optimization</subject><subject>Recognition</subject><subject>Sensor phenomena and characterization</subject><subject>Sensors</subject><subject>Sign language</subject><subject>Skin</subject><subject>skin stretch</subject><subject>soft sensing</subject><subject>Strain</subject><subject>Transdermal medication</subject><subject>Virtual reality</subject><subject>Wrist</subject><issn>0278-0046</issn><issn>1557-9948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1LAzEQxYMoWKt3wUvA89bJV7M5aukXFBRbzyGbndht627Nbg_-96a2CAMzDO_NPH6E3DMYMAbmaTUfDzgwM-CGySFnF6THlNKZMTK_JD3gOs8A5PCa3LTtBoBJxVSPzJZdxM6vXbFDitlyW9X0zaUFDU2kU2y7Q0T6jr75rKuuamqaqlsjfXF-S5vwN89cXd6Sq-B2Ld6de598TMar0SxbvE7no-dF5lOwLpNehaCAK89l4aSSEnmuQymwKLxClMGVgoFjQahccibAh8KVXoeCc12Uok8eT3f3sfk-pHx20xxinV5aznMlhGKgkwpOKh-bto0Y7D5WXy7-WAb2yMsmXvbIy555JcvDyVIh4r8812CM1uIX0Kllpw</recordid><startdate>202001</startdate><enddate>202001</enddate><creator>Jiang, Shuo</creator><creator>Li, Ling</creator><creator>Xu, Haipeng</creator><creator>Xu, Junkai</creator><creator>Gu, Guoying</creator><creator>Shull, Peter B.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-7778-4523</orcidid><orcidid>https://orcid.org/0000-0003-2770-7295</orcidid><orcidid>https://orcid.org/0000-0001-8931-5743</orcidid><orcidid>https://orcid.org/0000-0003-3645-6301</orcidid><orcidid>https://orcid.org/0000-0002-3215-6136</orcidid></search><sort><creationdate>202001</creationdate><title>Stretchable e-Skin Patch for Gesture Recognition on the Back of the Hand</title><author>Jiang, Shuo ; Li, Ling ; Xu, Haipeng ; Xu, Junkai ; Gu, Guoying ; Shull, Peter B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-4c5ff5025c24ba4544e287fd3ebbc5ee4fad310a1f35842130cfbadc7fb227bd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Augmented reality</topic><topic>Classification</topic><topic>Feature selection</topic><topic>Finger jointing</topic><topic>Forearm</topic><topic>Gesture recognition</topic><topic>Human motion</topic><topic>human–computer interaction</topic><topic>Muscles</topic><topic>Optimization</topic><topic>Recognition</topic><topic>Sensor phenomena and characterization</topic><topic>Sensors</topic><topic>Sign language</topic><topic>Skin</topic><topic>skin stretch</topic><topic>soft sensing</topic><topic>Strain</topic><topic>Transdermal medication</topic><topic>Virtual reality</topic><topic>Wrist</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Shuo</creatorcontrib><creatorcontrib>Li, Ling</creatorcontrib><creatorcontrib>Xu, Haipeng</creatorcontrib><creatorcontrib>Xu, Junkai</creatorcontrib><creatorcontrib>Gu, Guoying</creatorcontrib><creatorcontrib>Shull, Peter B.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on industrial electronics (1982)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jiang, Shuo</au><au>Li, Ling</au><au>Xu, Haipeng</au><au>Xu, Junkai</au><au>Gu, Guoying</au><au>Shull, Peter B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Stretchable e-Skin Patch for Gesture Recognition on the Back of the Hand</atitle><jtitle>IEEE transactions on industrial electronics (1982)</jtitle><stitle>TIE</stitle><date>2020-01</date><risdate>2020</risdate><volume>67</volume><issue>1</issue><spage>647</spage><epage>657</epage><pages>647-657</pages><issn>0278-0046</issn><eissn>1557-9948</eissn><coden>ITIED6</coden><abstract>Gesture recognition is important for human-computer interaction and a variety of emerging research and commercial areas including virtual and augmented reality. Current approaches typically require sensors to be placed on the forearm, wrist, or directly across finger joints; however, they can be cumbersome or hinder human movement and sensation. In this paper, we introduce a novel approach to recognize hand gestures by estimating skin strain with multiple soft sensors optimally placed across the back of the hand. A pilot study was first conducted by covering the back of the hand with 40 small 2.5 mm reflective markers and using a high-precision camera system to measure skin strain patterns for individual finger movements. Optimal strain locations are then determined and used for sensor placement in a stretchable e-skin patch prototype. Experimental testing is performed to evaluate the stretchable e-skin patch performance in classifying individual finger gestures and American Sign Language 0-9 number gestures. Results showed classification accuracies of 95.3% and 94.4% for finger gestures and American Sign Language 0-9 gestures, respectively. 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subjects | Augmented reality Classification Feature selection Finger jointing Forearm Gesture recognition Human motion human–computer interaction Muscles Optimization Recognition Sensor phenomena and characterization Sensors Sign language Skin skin stretch soft sensing Strain Transdermal medication Virtual reality Wrist |
title | Stretchable e-Skin Patch for Gesture Recognition on the Back of the Hand |
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