An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation
This paper presents a novel electromyography (EMG)-driven hand exoskeleton for bilateral rehabilitation of grasping in stroke. The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to patient's hand size, and (b) free-palm and free...
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| Veröffentlicht in: | IEEE transactions on haptics 2015-04, Vol.8 (2), p.140-151 |
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| creator | Leonardis, Daniele Chisari, Carmelo Bergamasco, Massimo Frisoli, Antonio Barsotti, Michele Loconsole, Claudio Solazzi, Massimiliano Troncossi, Marco Mazzotti, Claudio Castelli, Vincenzo Parenti Procopio, Caterina Lamola, Giuseppe |
| description | This paper presents a novel electromyography (EMG)-driven hand exoskeleton for bilateral rehabilitation of grasping in stroke. The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to patient's hand size, and (b) free-palm and free-fingertip design, preserving the residual sensory perceptual capability of touch during assistance in grasping of real objects. In the envisaged bilateral training strategy, the patient's non paretic hand acted as guidance for the paretic hand in grasping tasks. Grasping force exerted by the non paretic hand was estimated in real-time from EMG signals, and then replicated as robotic assistance for the paretic hand by means of the hand-exoskeleton. Estimation of the grasping force through EMG allowed to perform rehabilitation exercises with any, non sensorized, graspable objects. This paper presents the system design, development, and experimental evaluation. Experiments were performed within a group of six healthy subjects and two chronic stroke patients, executing robotic-assisted grasping tasks. Results related to performance in estimation and modulation of the robotic assistance, and to the outcomes of the pilot rehabilitation sessions with stroke patients, positively support validity of the proposed approach for application in stroke rehabilitation. |
| doi_str_mv | 10.1109/TOH.2015.2417570 |
| format | Article |
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The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to patient's hand size, and (b) free-palm and free-fingertip design, preserving the residual sensory perceptual capability of touch during assistance in grasping of real objects. In the envisaged bilateral training strategy, the patient's non paretic hand acted as guidance for the paretic hand in grasping tasks. Grasping force exerted by the non paretic hand was estimated in real-time from EMG signals, and then replicated as robotic assistance for the paretic hand by means of the hand-exoskeleton. Estimation of the grasping force through EMG allowed to perform rehabilitation exercises with any, non sensorized, graspable objects. This paper presents the system design, development, and experimental evaluation. Experiments were performed within a group of six healthy subjects and two chronic stroke patients, executing robotic-assisted grasping tasks. Results related to performance in estimation and modulation of the robotic assistance, and to the outcomes of the pilot rehabilitation sessions with stroke patients, positively support validity of the proposed approach for application in stroke rehabilitation.</description><identifier>ISSN: 1939-1412</identifier><identifier>EISSN: 2329-4051</identifier><identifier>DOI: 10.1109/TOH.2015.2417570</identifier><identifier>PMID: 25838528</identifier><identifier>CODEN: ITHEBX</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>C.2.0.c Emerging technologies ; Electromyography ; Exoskeleton Device ; Exoskeletons ; Fingers - physiology ; Force ; Grasping ; Grasping force ; H.1.2 User/Machine Systems ; H.1.2.b Human-centere d computing ; H.5.2.g Haptic I/O ; Hand Strength - physiology ; Humans ; I.2.9 Robotics ; J.3.b Health ; L.1 Human Haptics ; L.1.0.b Biomechanics ; L.3.0 Integrating touchbased interactions into various domains Assistive technology ; L.3.0.l Rehabilitation ; Orthotic Devices ; Patients ; Rehabilitation ; Robot sensing systems ; Robotics ; Robotics - instrumentation ; Robotics - methods ; Stroke ; Stroke - physiopathology ; Stroke Rehabilitation ; Strokes ; Tasks ; Training</subject><ispartof>IEEE transactions on haptics, 2015-04, Vol.8 (2), p.140-151</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Apr/Jun 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c535t-5a5bc51a45091ef1f03c3042a48ae3f67bdbed7f4af1ee45f3596ec8aab8e9023</citedby><cites>FETCH-LOGICAL-c535t-5a5bc51a45091ef1f03c3042a48ae3f67bdbed7f4af1ee45f3596ec8aab8e9023</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7072553$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27929,27930,54763</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7072553$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25838528$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Leonardis, Daniele</creatorcontrib><creatorcontrib>Chisari, Carmelo</creatorcontrib><creatorcontrib>Bergamasco, Massimo</creatorcontrib><creatorcontrib>Frisoli, Antonio</creatorcontrib><creatorcontrib>Barsotti, Michele</creatorcontrib><creatorcontrib>Loconsole, Claudio</creatorcontrib><creatorcontrib>Solazzi, Massimiliano</creatorcontrib><creatorcontrib>Troncossi, Marco</creatorcontrib><creatorcontrib>Mazzotti, Claudio</creatorcontrib><creatorcontrib>Castelli, Vincenzo Parenti</creatorcontrib><creatorcontrib>Procopio, Caterina</creatorcontrib><creatorcontrib>Lamola, Giuseppe</creatorcontrib><title>An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation</title><title>IEEE transactions on haptics</title><addtitle>TOH</addtitle><addtitle>IEEE Trans Haptics</addtitle><description>This paper presents a novel electromyography (EMG)-driven hand exoskeleton for bilateral rehabilitation of grasping in stroke. The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to patient's hand size, and (b) free-palm and free-fingertip design, preserving the residual sensory perceptual capability of touch during assistance in grasping of real objects. In the envisaged bilateral training strategy, the patient's non paretic hand acted as guidance for the paretic hand in grasping tasks. Grasping force exerted by the non paretic hand was estimated in real-time from EMG signals, and then replicated as robotic assistance for the paretic hand by means of the hand-exoskeleton. Estimation of the grasping force through EMG allowed to perform rehabilitation exercises with any, non sensorized, graspable objects. This paper presents the system design, development, and experimental evaluation. Experiments were performed within a group of six healthy subjects and two chronic stroke patients, executing robotic-assisted grasping tasks. Results related to performance in estimation and modulation of the robotic assistance, and to the outcomes of the pilot rehabilitation sessions with stroke patients, positively support validity of the proposed approach for application in stroke rehabilitation.</description><subject>C.2.0.c Emerging technologies</subject><subject>Electromyography</subject><subject>Exoskeleton Device</subject><subject>Exoskeletons</subject><subject>Fingers - physiology</subject><subject>Force</subject><subject>Grasping</subject><subject>Grasping force</subject><subject>H.1.2 User/Machine Systems</subject><subject>H.1.2.b Human-centere d computing</subject><subject>H.5.2.g Haptic I/O</subject><subject>Hand Strength - physiology</subject><subject>Humans</subject><subject>I.2.9 Robotics</subject><subject>J.3.b Health</subject><subject>L.1 Human Haptics</subject><subject>L.1.0.b Biomechanics</subject><subject>L.3.0 Integrating touchbased interactions into various domains Assistive technology</subject><subject>L.3.0.l Rehabilitation</subject><subject>Orthotic Devices</subject><subject>Patients</subject><subject>Rehabilitation</subject><subject>Robot sensing systems</subject><subject>Robotics</subject><subject>Robotics - instrumentation</subject><subject>Robotics - methods</subject><subject>Stroke</subject><subject>Stroke - physiopathology</subject><subject>Stroke Rehabilitation</subject><subject>Strokes</subject><subject>Tasks</subject><subject>Training</subject><issn>1939-1412</issn><issn>2329-4051</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqFkc9L40AYhgdxWavuXRAk4MVLut_8yswctdR2waVQ9BwmyTcYnWZ0JoXd_96E1h68ePoO7_O-8PEQckFhSimY34-r5ZQBlVMmqJIKjsiEcWZyAZIekwk13ORUUHZCTlN6ASiYMuInOWFScy2ZnpDFbZfN_y7yWej6GLzHJluHKvRtnS1t12TzfyG9osc-dJkLMbtrve0xWp-t8dlWrW9727ehOyc_nPUJf-3vGXm6nz_OlvnDavFndvuQ15LLPpdWVrWkVkgwFB11wGsOglmhLXJXqKqpsFFOWEcRhXRcmgJrbW2l0QDjZ-Rmt_sWw_sWU19u2lSj97bDsE0lVQUDpZSB79FiGAQt2Ihef0FfwjZ2wyMjJRnXWowU7Kg6hpQiuvItthsb_5cUytFHOfgoRx_l3sdQudoPb6sNNofCp4ABuNwBLSIeYgWKScn5B1aEjOw</recordid><startdate>201504</startdate><enddate>201504</enddate><creator>Leonardis, Daniele</creator><creator>Chisari, Carmelo</creator><creator>Bergamasco, Massimo</creator><creator>Frisoli, Antonio</creator><creator>Barsotti, Michele</creator><creator>Loconsole, Claudio</creator><creator>Solazzi, Massimiliano</creator><creator>Troncossi, Marco</creator><creator>Mazzotti, Claudio</creator><creator>Castelli, Vincenzo Parenti</creator><creator>Procopio, Caterina</creator><creator>Lamola, Giuseppe</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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physiology</topic><topic>Force</topic><topic>Grasping</topic><topic>Grasping force</topic><topic>H.1.2 User/Machine Systems</topic><topic>H.1.2.b Human-centere d computing</topic><topic>H.5.2.g Haptic I/O</topic><topic>Hand Strength - physiology</topic><topic>Humans</topic><topic>I.2.9 Robotics</topic><topic>J.3.b Health</topic><topic>L.1 Human Haptics</topic><topic>L.1.0.b Biomechanics</topic><topic>L.3.0 Integrating touchbased interactions into various domains Assistive technology</topic><topic>L.3.0.l Rehabilitation</topic><topic>Orthotic Devices</topic><topic>Patients</topic><topic>Rehabilitation</topic><topic>Robot sensing systems</topic><topic>Robotics</topic><topic>Robotics - instrumentation</topic><topic>Robotics - methods</topic><topic>Stroke</topic><topic>Stroke - physiopathology</topic><topic>Stroke Rehabilitation</topic><topic>Strokes</topic><topic>Tasks</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Leonardis, Daniele</creatorcontrib><creatorcontrib>Chisari, Carmelo</creatorcontrib><creatorcontrib>Bergamasco, Massimo</creatorcontrib><creatorcontrib>Frisoli, Antonio</creatorcontrib><creatorcontrib>Barsotti, Michele</creatorcontrib><creatorcontrib>Loconsole, Claudio</creatorcontrib><creatorcontrib>Solazzi, Massimiliano</creatorcontrib><creatorcontrib>Troncossi, Marco</creatorcontrib><creatorcontrib>Mazzotti, Claudio</creatorcontrib><creatorcontrib>Castelli, Vincenzo Parenti</creatorcontrib><creatorcontrib>Procopio, Caterina</creatorcontrib><creatorcontrib>Lamola, Giuseppe</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>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</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><collection>MEDLINE - Academic</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on haptics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Leonardis, Daniele</au><au>Chisari, Carmelo</au><au>Bergamasco, Massimo</au><au>Frisoli, Antonio</au><au>Barsotti, Michele</au><au>Loconsole, Claudio</au><au>Solazzi, Massimiliano</au><au>Troncossi, Marco</au><au>Mazzotti, Claudio</au><au>Castelli, Vincenzo Parenti</au><au>Procopio, Caterina</au><au>Lamola, Giuseppe</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation</atitle><jtitle>IEEE transactions on haptics</jtitle><stitle>TOH</stitle><addtitle>IEEE Trans Haptics</addtitle><date>2015-04</date><risdate>2015</risdate><volume>8</volume><issue>2</issue><spage>140</spage><epage>151</epage><pages>140-151</pages><issn>1939-1412</issn><eissn>2329-4051</eissn><coden>ITHEBX</coden><abstract>This paper presents a novel electromyography (EMG)-driven hand exoskeleton for bilateral rehabilitation of grasping in stroke. The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to patient's hand size, and (b) free-palm and free-fingertip design, preserving the residual sensory perceptual capability of touch during assistance in grasping of real objects. In the envisaged bilateral training strategy, the patient's non paretic hand acted as guidance for the paretic hand in grasping tasks. Grasping force exerted by the non paretic hand was estimated in real-time from EMG signals, and then replicated as robotic assistance for the paretic hand by means of the hand-exoskeleton. Estimation of the grasping force through EMG allowed to perform rehabilitation exercises with any, non sensorized, graspable objects. This paper presents the system design, development, and experimental evaluation. Experiments were performed within a group of six healthy subjects and two chronic stroke patients, executing robotic-assisted grasping tasks. Results related to performance in estimation and modulation of the robotic assistance, and to the outcomes of the pilot rehabilitation sessions with stroke patients, positively support validity of the proposed approach for application in stroke rehabilitation.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>25838528</pmid><doi>10.1109/TOH.2015.2417570</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | C.2.0.c Emerging technologies Electromyography Exoskeleton Device Exoskeletons Fingers - physiology Force Grasping Grasping force H.1.2 User/Machine Systems H.1.2.b Human-centere d computing H.5.2.g Haptic I/O Hand Strength - physiology Humans I.2.9 Robotics J.3.b Health L.1 Human Haptics L.1.0.b Biomechanics L.3.0 Integrating touchbased interactions into various domains Assistive technology L.3.0.l Rehabilitation Orthotic Devices Patients Rehabilitation Robot sensing systems Robotics Robotics - instrumentation Robotics - methods Stroke Stroke - physiopathology Stroke Rehabilitation Strokes Tasks Training |
| title | An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation |
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