Real-Time Myocontrol of a Human-Computer Interface by Paretic Muscles After Stroke
Biosignals from skeletal muscle have been used to control human-machine interface. Signals from paretic muscles of humans with stroke are distorted and highly variable. Here, we examine the stability of surface electromyography (sEMG) features from paretic hand muscles to enable continuous, real-tim...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on cognitive and developmental systems 2018-12, Vol.10 (4), p.1126-1132 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1132 |
|---|---|
| container_issue | 4 |
| container_start_page | 1126 |
| container_title | IEEE transactions on cognitive and developmental systems |
| container_volume | 10 |
| creator | Chun Yang Jinyi Long Urbin, M. A. Yanyun Feng Ge Song Jian Weng Zhijun Li |
| description | Biosignals from skeletal muscle have been used to control human-machine interface. Signals from paretic muscles of humans with stroke are distorted and highly variable. Here, we examine the stability of surface electromyography (sEMG) features from paretic hand muscles to enable continuous, real-time multicommand control of a human-computer interface (HC!). Subjects with long standing cortical strokes (>6 months, n = 12) and neurologically intact controls (n = 12) performed two wrist rotations (wrist extend and wrist flex) and two grips (power grip and fine grip) with the nondominant (controls) or paretic (stroke patients) hand. Data reduction analyses revealed a distinct pattern of coactivation across muscles for each gesture. These synergies were similar for control and stroke groups and stable across sessions. Results of offline experiments involving wrist rotation and hand grips confirmed that gestures performed in isolation or combination were recognized at greater than chance level in both groups (p |
| doi_str_mv | 10.1109/TCDS.2018.2830388 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TCDS_2018_2830388</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8350308</ieee_id><sourcerecordid>2299148232</sourcerecordid><originalsourceid>FETCH-LOGICAL-c359t-e338bcfab555540e61d55342a798a56d2e8fb2c4c80892924c3c442b39609183</originalsourceid><addsrcrecordid>eNo9kE1rwzAMhs3YYKXrDxi7GHZOZ1tOYh9L9tFCy0abu3FcBdI1ceckh_77JbTUB1mH55XEQ8gzZ3POmX7Ls_fdXDCu5kIBA6XuyERAqiOlQd_fesEeyaxtD4wxnkCqZDoh2y3aY5RXNdLN2TvfdMEfqS-ppcu-tk2U-frUdxjoqhlqaR3S4kx_bMCucnTTt-6ILV2UI7Ibwr_4RB5Ke2xxdv2nJP_8yLNltP7-WmWLdeQg1l2EAKpwpS3i4UmGCd_HMUhhU61snOwFqrIQTjrFhtO1kA6clKIAnTDNFUzJ62XsKfi_HtvOHHwfmmGjEUJrLpUAMVD8Qrng2zZgaU6hqm04G87MKM-M8swoz1zlDZmXS6ZCxBuvIGbAFPwDkkNpEw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2299148232</pqid></control><display><type>article</type><title>Real-Time Myocontrol of a Human-Computer Interface by Paretic Muscles After Stroke</title><source>IEEE Electronic Library (IEL)</source><creator>Chun Yang ; Jinyi Long ; Urbin, M. A. ; Yanyun Feng ; Ge Song ; Jian Weng ; Zhijun Li</creator><creatorcontrib>Chun Yang ; Jinyi Long ; Urbin, M. A. ; Yanyun Feng ; Ge Song ; Jian Weng ; Zhijun Li</creatorcontrib><description>Biosignals from skeletal muscle have been used to control human-machine interface. Signals from paretic muscles of humans with stroke are distorted and highly variable. Here, we examine the stability of surface electromyography (sEMG) features from paretic hand muscles to enable continuous, real-time multicommand control of a human-computer interface (HC!). Subjects with long standing cortical strokes (>6 months, n = 12) and neurologically intact controls (n = 12) performed two wrist rotations (wrist extend and wrist flex) and two grips (power grip and fine grip) with the nondominant (controls) or paretic (stroke patients) hand. Data reduction analyses revealed a distinct pattern of coactivation across muscles for each gesture. These synergies were similar for control and stroke groups and stable across sessions. Results of offline experiments involving wrist rotation and hand grips confirmed that gestures performed in isolation or combination were recognized at greater than chance level in both groups (p <; 0.01). !n online experiments, HC! control was evaluated with a balloon shooter game. Users in both groups were able to control the direction and speed of a simulated bullet to a balloon target with greater than chance-level accuracy (p <; 0.01). Taken together, these results demonstrate that sEMG synergy features from paretic hand muscles can be used to drive continuous, real-time multicommand control of an HC!.</description><identifier>ISSN: 2379-8920</identifier><identifier>EISSN: 2379-8939</identifier><identifier>DOI: 10.1109/TCDS.2018.2830388</identifier><identifier>CODEN: ITCDA4</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Balloons ; Biomedical signal processing ; Computer & video games ; Computer simulation ; Data reduction ; Electromyography ; hand gesture recognition ; Hand tools ; hemiparesis ; Human computer interaction ; Human-computer interface ; human–computer interface (HCI) ; Man-machine interfaces ; Muscles ; Real time ; Real-time systems ; rehabilitation ; Stroke ; Surface stability ; Task analysis ; Training ; Wrist</subject><ispartof>IEEE transactions on cognitive and developmental systems, 2018-12, Vol.10 (4), p.1126-1132</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-e338bcfab555540e61d55342a798a56d2e8fb2c4c80892924c3c442b39609183</citedby><cites>FETCH-LOGICAL-c359t-e338bcfab555540e61d55342a798a56d2e8fb2c4c80892924c3c442b39609183</cites><orcidid>0000-0001-6150-987X ; 0000-0002-4820-5174 ; 0000-0002-3909-488X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8350308$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8350308$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Chun Yang</creatorcontrib><creatorcontrib>Jinyi Long</creatorcontrib><creatorcontrib>Urbin, M. A.</creatorcontrib><creatorcontrib>Yanyun Feng</creatorcontrib><creatorcontrib>Ge Song</creatorcontrib><creatorcontrib>Jian Weng</creatorcontrib><creatorcontrib>Zhijun Li</creatorcontrib><title>Real-Time Myocontrol of a Human-Computer Interface by Paretic Muscles After Stroke</title><title>IEEE transactions on cognitive and developmental systems</title><addtitle>TCDS</addtitle><description>Biosignals from skeletal muscle have been used to control human-machine interface. Signals from paretic muscles of humans with stroke are distorted and highly variable. Here, we examine the stability of surface electromyography (sEMG) features from paretic hand muscles to enable continuous, real-time multicommand control of a human-computer interface (HC!). Subjects with long standing cortical strokes (>6 months, n = 12) and neurologically intact controls (n = 12) performed two wrist rotations (wrist extend and wrist flex) and two grips (power grip and fine grip) with the nondominant (controls) or paretic (stroke patients) hand. Data reduction analyses revealed a distinct pattern of coactivation across muscles for each gesture. These synergies were similar for control and stroke groups and stable across sessions. Results of offline experiments involving wrist rotation and hand grips confirmed that gestures performed in isolation or combination were recognized at greater than chance level in both groups (p <; 0.01). !n online experiments, HC! control was evaluated with a balloon shooter game. Users in both groups were able to control the direction and speed of a simulated bullet to a balloon target with greater than chance-level accuracy (p <; 0.01). Taken together, these results demonstrate that sEMG synergy features from paretic hand muscles can be used to drive continuous, real-time multicommand control of an HC!.</description><subject>Balloons</subject><subject>Biomedical signal processing</subject><subject>Computer & video games</subject><subject>Computer simulation</subject><subject>Data reduction</subject><subject>Electromyography</subject><subject>hand gesture recognition</subject><subject>Hand tools</subject><subject>hemiparesis</subject><subject>Human computer interaction</subject><subject>Human-computer interface</subject><subject>human–computer interface (HCI)</subject><subject>Man-machine interfaces</subject><subject>Muscles</subject><subject>Real time</subject><subject>Real-time systems</subject><subject>rehabilitation</subject><subject>Stroke</subject><subject>Surface stability</subject><subject>Task analysis</subject><subject>Training</subject><subject>Wrist</subject><issn>2379-8920</issn><issn>2379-8939</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1rwzAMhs3YYKXrDxi7GHZOZ1tOYh9L9tFCy0abu3FcBdI1ceckh_77JbTUB1mH55XEQ8gzZ3POmX7Ls_fdXDCu5kIBA6XuyERAqiOlQd_fesEeyaxtD4wxnkCqZDoh2y3aY5RXNdLN2TvfdMEfqS-ppcu-tk2U-frUdxjoqhlqaR3S4kx_bMCucnTTt-6ILV2UI7Ibwr_4RB5Ke2xxdv2nJP_8yLNltP7-WmWLdeQg1l2EAKpwpS3i4UmGCd_HMUhhU61snOwFqrIQTjrFhtO1kA6clKIAnTDNFUzJ62XsKfi_HtvOHHwfmmGjEUJrLpUAMVD8Qrng2zZgaU6hqm04G87MKM-M8swoz1zlDZmXS6ZCxBuvIGbAFPwDkkNpEw</recordid><startdate>20181201</startdate><enddate>20181201</enddate><creator>Chun Yang</creator><creator>Jinyi Long</creator><creator>Urbin, M. A.</creator><creator>Yanyun Feng</creator><creator>Ge Song</creator><creator>Jian Weng</creator><creator>Zhijun Li</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>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-6150-987X</orcidid><orcidid>https://orcid.org/0000-0002-4820-5174</orcidid><orcidid>https://orcid.org/0000-0002-3909-488X</orcidid></search><sort><creationdate>20181201</creationdate><title>Real-Time Myocontrol of a Human-Computer Interface by Paretic Muscles After Stroke</title><author>Chun Yang ; Jinyi Long ; Urbin, M. A. ; Yanyun Feng ; Ge Song ; Jian Weng ; Zhijun Li</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-e338bcfab555540e61d55342a798a56d2e8fb2c4c80892924c3c442b39609183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Balloons</topic><topic>Biomedical signal processing</topic><topic>Computer & video games</topic><topic>Computer simulation</topic><topic>Data reduction</topic><topic>Electromyography</topic><topic>hand gesture recognition</topic><topic>Hand tools</topic><topic>hemiparesis</topic><topic>Human computer interaction</topic><topic>Human-computer interface</topic><topic>human–computer interface (HCI)</topic><topic>Man-machine interfaces</topic><topic>Muscles</topic><topic>Real time</topic><topic>Real-time systems</topic><topic>rehabilitation</topic><topic>Stroke</topic><topic>Surface stability</topic><topic>Task analysis</topic><topic>Training</topic><topic>Wrist</topic><toplevel>online_resources</toplevel><creatorcontrib>Chun Yang</creatorcontrib><creatorcontrib>Jinyi Long</creatorcontrib><creatorcontrib>Urbin, M. A.</creatorcontrib><creatorcontrib>Yanyun Feng</creatorcontrib><creatorcontrib>Ge Song</creatorcontrib><creatorcontrib>Jian Weng</creatorcontrib><creatorcontrib>Zhijun Li</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>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</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>IEEE transactions on cognitive and developmental systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chun Yang</au><au>Jinyi Long</au><au>Urbin, M. A.</au><au>Yanyun Feng</au><au>Ge Song</au><au>Jian Weng</au><au>Zhijun Li</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real-Time Myocontrol of a Human-Computer Interface by Paretic Muscles After Stroke</atitle><jtitle>IEEE transactions on cognitive and developmental systems</jtitle><stitle>TCDS</stitle><date>2018-12-01</date><risdate>2018</risdate><volume>10</volume><issue>4</issue><spage>1126</spage><epage>1132</epage><pages>1126-1132</pages><issn>2379-8920</issn><eissn>2379-8939</eissn><coden>ITCDA4</coden><abstract>Biosignals from skeletal muscle have been used to control human-machine interface. Signals from paretic muscles of humans with stroke are distorted and highly variable. Here, we examine the stability of surface electromyography (sEMG) features from paretic hand muscles to enable continuous, real-time multicommand control of a human-computer interface (HC!). Subjects with long standing cortical strokes (>6 months, n = 12) and neurologically intact controls (n = 12) performed two wrist rotations (wrist extend and wrist flex) and two grips (power grip and fine grip) with the nondominant (controls) or paretic (stroke patients) hand. Data reduction analyses revealed a distinct pattern of coactivation across muscles for each gesture. These synergies were similar for control and stroke groups and stable across sessions. Results of offline experiments involving wrist rotation and hand grips confirmed that gestures performed in isolation or combination were recognized at greater than chance level in both groups (p <; 0.01). !n online experiments, HC! control was evaluated with a balloon shooter game. Users in both groups were able to control the direction and speed of a simulated bullet to a balloon target with greater than chance-level accuracy (p <; 0.01). Taken together, these results demonstrate that sEMG synergy features from paretic hand muscles can be used to drive continuous, real-time multicommand control of an HC!.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TCDS.2018.2830388</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0001-6150-987X</orcidid><orcidid>https://orcid.org/0000-0002-4820-5174</orcidid><orcidid>https://orcid.org/0000-0002-3909-488X</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2379-8920 |
| ispartof | IEEE transactions on cognitive and developmental systems, 2018-12, Vol.10 (4), p.1126-1132 |
| issn | 2379-8920 2379-8939 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_TCDS_2018_2830388 |
| source | IEEE Electronic Library (IEL) |
| subjects | Balloons Biomedical signal processing Computer & video games Computer simulation Data reduction Electromyography hand gesture recognition Hand tools hemiparesis Human computer interaction Human-computer interface human–computer interface (HCI) Man-machine interfaces Muscles Real time Real-time systems rehabilitation Stroke Surface stability Task analysis Training Wrist |
| title | Real-Time Myocontrol of a Human-Computer Interface by Paretic Muscles After Stroke |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T18%3A47%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Real-Time%20Myocontrol%20of%20a%20Human-Computer%20Interface%20by%20Paretic%20Muscles%20After%20Stroke&rft.jtitle=IEEE%20transactions%20on%20cognitive%20and%20developmental%20systems&rft.au=Chun%20Yang&rft.date=2018-12-01&rft.volume=10&rft.issue=4&rft.spage=1126&rft.epage=1132&rft.pages=1126-1132&rft.issn=2379-8920&rft.eissn=2379-8939&rft.coden=ITCDA4&rft_id=info:doi/10.1109/TCDS.2018.2830388&rft_dat=%3Cproquest_RIE%3E2299148232%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2299148232&rft_id=info:pmid/&rft_ieee_id=8350308&rfr_iscdi=true |