Robotic Leg Control with EMG Decoding in an Amputee with Nerve Transfers
A 31-year-old man who underwent knee-disarticulation amputation had improved control of a robotic leg prosthesis with the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles. Summary The clinical application of robotic technology to powe...
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| Veröffentlicht in: | The New England journal of medicine 2013-09, Vol.369 (13), p.1237-1242 |
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| container_title | The New England journal of medicine |
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| creator | Hargrove, Levi J Simon, Ann M Young, Aaron J Lipschutz, Robert D Finucane, Suzanne B Smith, Douglas G Kuiken, Todd A |
| description | A 31-year-old man who underwent knee-disarticulation amputation had improved control of a robotic leg prosthesis with the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles.
Summary
The clinical application of robotic technology to powered prosthetic knees and ankles is limited by the lack of a robust control strategy. We found that the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles in a patient who had undergone knee amputation improved control of a robotic leg prosthesis. EMG signals were decoded with a pattern-recognition algorithm and combined with data from sensors on the prosthesis to interpret the patient's intended movements. This provided robust and intuitive control of ambulation — with seamless transitions between walking on level ground, stairs, and ramps — . . . |
| doi_str_mv | 10.1056/NEJMoa1300126 |
| format | Article |
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Summary
The clinical application of robotic technology to powered prosthetic knees and ankles is limited by the lack of a robust control strategy. We found that the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles in a patient who had undergone knee amputation improved control of a robotic leg prosthesis. EMG signals were decoded with a pattern-recognition algorithm and combined with data from sensors on the prosthesis to interpret the patient's intended movements. This provided robust and intuitive control of ambulation — with seamless transitions between walking on level ground, stairs, and ramps — . . .</description><identifier>ISSN: 0028-4793</identifier><identifier>EISSN: 1533-4406</identifier><identifier>DOI: 10.1056/NEJMoa1300126</identifier><identifier>PMID: 24066744</identifier><identifier>CODEN: NEJMAG</identifier><language>eng</language><publisher>Waltham, MA: Massachusetts Medical Society</publisher><subject>Accidents, Traffic ; Accuracy ; Adult ; Amputation ; Amputation - methods ; Amputees - rehabilitation ; Ankle ; Artificial Limbs ; Biological and medical sciences ; Classification ; Cranial nerves. Peripheral nerves. Autonomic nervous system ; Data processing ; Electrodes ; Electrodiagnosis. Electric activity recording ; Electromyography ; General aspects ; Humans ; Investigative techniques, diagnostic techniques (general aspects) ; Knee ; Laboratories ; Leg ; Leg - innervation ; Leg - physiology ; Leg - surgery ; Medical research ; Medical sciences ; Motorcycles ; Muscle, Skeletal - innervation ; Muscle, Skeletal - physiology ; Muscle, Skeletal - surgery ; Muscles ; Nerve Transfer ; Nervous system ; Neurosurgery ; Posture ; Prostheses ; Robotic surgery ; Robotics ; Sensors ; Surgery ; Surgery (general aspects). Transplantations, organ and tissue grafts. Graft diseases ; Walking ; Walking - physiology</subject><ispartof>The New England journal of medicine, 2013-09, Vol.369 (13), p.1237-1242</ispartof><rights>Copyright © 2013 Massachusetts Medical Society. All rights reserved.</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c464t-67eaa80161a3aa7b4f41e95379954136ab9c7f62b877039cbc4609fa43ae605b3</citedby><cites>FETCH-LOGICAL-c464t-67eaa80161a3aa7b4f41e95379954136ab9c7f62b877039cbc4609fa43ae605b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.nejm.org/doi/pdf/10.1056/NEJMoa1300126$$EPDF$$P50$$Gmms$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1437186097?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,2759,2760,26103,27924,27925,52382,54064,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27697465$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24066744$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hargrove, Levi J</creatorcontrib><creatorcontrib>Simon, Ann M</creatorcontrib><creatorcontrib>Young, Aaron J</creatorcontrib><creatorcontrib>Lipschutz, Robert D</creatorcontrib><creatorcontrib>Finucane, Suzanne B</creatorcontrib><creatorcontrib>Smith, Douglas G</creatorcontrib><creatorcontrib>Kuiken, Todd A</creatorcontrib><title>Robotic Leg Control with EMG Decoding in an Amputee with Nerve Transfers</title><title>The New England journal of medicine</title><addtitle>N Engl J Med</addtitle><description>A 31-year-old man who underwent knee-disarticulation amputation had improved control of a robotic leg prosthesis with the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles.
Summary
The clinical application of robotic technology to powered prosthetic knees and ankles is limited by the lack of a robust control strategy. We found that the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles in a patient who had undergone knee amputation improved control of a robotic leg prosthesis. EMG signals were decoded with a pattern-recognition algorithm and combined with data from sensors on the prosthesis to interpret the patient's intended movements. This provided robust and intuitive control of ambulation — with seamless transitions between walking on level ground, stairs, and ramps — . . .</description><subject>Accidents, Traffic</subject><subject>Accuracy</subject><subject>Adult</subject><subject>Amputation</subject><subject>Amputation - methods</subject><subject>Amputees - rehabilitation</subject><subject>Ankle</subject><subject>Artificial Limbs</subject><subject>Biological and medical sciences</subject><subject>Classification</subject><subject>Cranial nerves. Peripheral nerves. Autonomic nervous system</subject><subject>Data processing</subject><subject>Electrodes</subject><subject>Electrodiagnosis. Electric activity recording</subject><subject>Electromyography</subject><subject>General aspects</subject><subject>Humans</subject><subject>Investigative techniques, diagnostic techniques (general aspects)</subject><subject>Knee</subject><subject>Laboratories</subject><subject>Leg</subject><subject>Leg - innervation</subject><subject>Leg - physiology</subject><subject>Leg - surgery</subject><subject>Medical research</subject><subject>Medical sciences</subject><subject>Motorcycles</subject><subject>Muscle, Skeletal - innervation</subject><subject>Muscle, Skeletal - physiology</subject><subject>Muscle, Skeletal - surgery</subject><subject>Muscles</subject><subject>Nerve Transfer</subject><subject>Nervous system</subject><subject>Neurosurgery</subject><subject>Posture</subject><subject>Prostheses</subject><subject>Robotic surgery</subject><subject>Robotics</subject><subject>Sensors</subject><subject>Surgery</subject><subject>Surgery (general aspects). Transplantations, organ and tissue grafts. Graft diseases</subject><subject>Walking</subject><subject>Walking - physiology</subject><issn>0028-4793</issn><issn>1533-4406</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp10M1LwzAYBvAgis6Po1cJiOClmjRp0hzHnJuyTZB5Lml8qx1tM5NW8b83uulQMJcc3l-evDwIHVNyQUkiLmfD26nVlBFCY7GFejRhLOKciG3UIyROIy4V20P73i9IOJSrXbQXh7mQnPfQ-N7mti0NnsATHtimdbbCb2X7jIfTEb4CYx_L5gmXDdYN7tfLrgVYzWfgXgHPnW58Ac4fop1CVx6O1vcBergezgfjaHI3uhn0J5HhgreRkKB1Sqigmmktc15wCiphUqmEUyZ0rowsRJynUhKmTB6eEVVozjQIkuTsAJ2vcpfOvnTg26wuvYGq0g3YzmeUM5mkMVNxoKd_6MJ2rgnbfSmahmQZVLRSxlnvHRTZ0pW1du8ZJdlnxdmvioM_Wad2eQ2PP_q70wDO1kB7o6siNGRKv3FSKMlFsnF17bMGFvU_H34ARg6L6g</recordid><startdate>20130926</startdate><enddate>20130926</enddate><creator>Hargrove, Levi J</creator><creator>Simon, Ann M</creator><creator>Young, Aaron J</creator><creator>Lipschutz, Robert D</creator><creator>Finucane, Suzanne B</creator><creator>Smith, Douglas G</creator><creator>Kuiken, Todd A</creator><general>Massachusetts Medical Society</general><scope>IQODW</scope><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>0TZ</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AN0</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K0Y</scope><scope>LK8</scope><scope>M0R</scope><scope>M0T</scope><scope>M1P</scope><scope>M2M</scope><scope>M2O</scope><scope>M2P</scope><scope>M7P</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20130926</creationdate><title>Robotic Leg Control with EMG Decoding in an Amputee with Nerve Transfers</title><author>Hargrove, Levi J ; Simon, Ann M ; Young, Aaron J ; Lipschutz, Robert D ; Finucane, Suzanne B ; Smith, Douglas G ; Kuiken, Todd A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c464t-67eaa80161a3aa7b4f41e95379954136ab9c7f62b877039cbc4609fa43ae605b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Accidents, Traffic</topic><topic>Accuracy</topic><topic>Adult</topic><topic>Amputation</topic><topic>Amputation - methods</topic><topic>Amputees - rehabilitation</topic><topic>Ankle</topic><topic>Artificial Limbs</topic><topic>Biological and medical sciences</topic><topic>Classification</topic><topic>Cranial nerves. Peripheral nerves. Autonomic nervous system</topic><topic>Data processing</topic><topic>Electrodes</topic><topic>Electrodiagnosis. Electric activity recording</topic><topic>Electromyography</topic><topic>General aspects</topic><topic>Humans</topic><topic>Investigative techniques, diagnostic techniques (general aspects)</topic><topic>Knee</topic><topic>Laboratories</topic><topic>Leg</topic><topic>Leg - innervation</topic><topic>Leg - physiology</topic><topic>Leg - surgery</topic><topic>Medical research</topic><topic>Medical sciences</topic><topic>Motorcycles</topic><topic>Muscle, Skeletal - innervation</topic><topic>Muscle, Skeletal - physiology</topic><topic>Muscle, Skeletal - surgery</topic><topic>Muscles</topic><topic>Nerve Transfer</topic><topic>Nervous system</topic><topic>Neurosurgery</topic><topic>Posture</topic><topic>Prostheses</topic><topic>Robotic surgery</topic><topic>Robotics</topic><topic>Sensors</topic><topic>Surgery</topic><topic>Surgery (general aspects). Transplantations, organ and tissue grafts. Graft diseases</topic><topic>Walking</topic><topic>Walking - physiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hargrove, Levi J</creatorcontrib><creatorcontrib>Simon, Ann M</creatorcontrib><creatorcontrib>Young, Aaron J</creatorcontrib><creatorcontrib>Lipschutz, Robert D</creatorcontrib><creatorcontrib>Finucane, Suzanne B</creatorcontrib><creatorcontrib>Smith, Douglas G</creatorcontrib><creatorcontrib>Kuiken, Todd A</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Pharma and Biotech Premium PRO</collection><collection>ProQuest Nursing and Allied Health Source</collection><collection>ProQuest_Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>British Nursing Database</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>eLibrary</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>New England Journal of Medicine</collection><collection>ProQuest Biological Science Collection</collection><collection>Family Health Database (Proquest)</collection><collection>Health Management Database (Proquest)</collection><collection>Medical Database</collection><collection>Psychology Database (ProQuest)</collection><collection>ProQuest research library</collection><collection>ProQuest Science Journals</collection><collection>ProQuest Biological Science Journals</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</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>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>The New England journal of medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hargrove, Levi J</au><au>Simon, Ann M</au><au>Young, Aaron J</au><au>Lipschutz, Robert D</au><au>Finucane, Suzanne B</au><au>Smith, Douglas G</au><au>Kuiken, Todd A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Robotic Leg Control with EMG Decoding in an Amputee with Nerve Transfers</atitle><jtitle>The New England journal of medicine</jtitle><addtitle>N Engl J Med</addtitle><date>2013-09-26</date><risdate>2013</risdate><volume>369</volume><issue>13</issue><spage>1237</spage><epage>1242</epage><pages>1237-1242</pages><issn>0028-4793</issn><eissn>1533-4406</eissn><coden>NEJMAG</coden><abstract>A 31-year-old man who underwent knee-disarticulation amputation had improved control of a robotic leg prosthesis with the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles.
Summary
The clinical application of robotic technology to powered prosthetic knees and ankles is limited by the lack of a robust control strategy. We found that the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles in a patient who had undergone knee amputation improved control of a robotic leg prosthesis. EMG signals were decoded with a pattern-recognition algorithm and combined with data from sensors on the prosthesis to interpret the patient's intended movements. This provided robust and intuitive control of ambulation — with seamless transitions between walking on level ground, stairs, and ramps — . . .</abstract><cop>Waltham, MA</cop><pub>Massachusetts Medical Society</pub><pmid>24066744</pmid><doi>10.1056/NEJMoa1300126</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Accidents, Traffic Accuracy Adult Amputation Amputation - methods Amputees - rehabilitation Ankle Artificial Limbs Biological and medical sciences Classification Cranial nerves. Peripheral nerves. Autonomic nervous system Data processing Electrodes Electrodiagnosis. Electric activity recording Electromyography General aspects Humans Investigative techniques, diagnostic techniques (general aspects) Knee Laboratories Leg Leg - innervation Leg - physiology Leg - surgery Medical research Medical sciences Motorcycles Muscle, Skeletal - innervation Muscle, Skeletal - physiology Muscle, Skeletal - surgery Muscles Nerve Transfer Nervous system Neurosurgery Posture Prostheses Robotic surgery Robotics Sensors Surgery Surgery (general aspects). Transplantations, organ and tissue grafts. Graft diseases Walking Walking - physiology |
| title | Robotic Leg Control with EMG Decoding in an Amputee with Nerve Transfers |
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