Real-Time, Simultaneous Myoelectric Control Using Force and Position-Based Training Paradigms
In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (posi...
Saved in:
| Published in | IEEE transactions on biomedical engineering Vol. 61; no. 2; pp. 279 - 287 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.02.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0018-9294 1558-2531 1558-2531 |
| DOI | 10.1109/TBME.2013.2281595 |
Cover
| Abstract | In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DOFs) (wrist flexion-extension, abduction-adduction, and pronation-supination) using mirrored bilateral contractions. In the resisted unconstrained experiment, some resistance to movement was provided using flexible wrist braces in order to increase the required level of muscle activation. The force, in constrained experiments, and position, in unconstrained and resisted unconstrained experiments, were measured. The three protocols were compared off-line using estimation accuracies (R 2 ) and online using a real-time computer-based target acquisition test. The constrained control paradigm outperformed the unconstrained method in the abduction-adduction DOF (R constrained 2 = 90.8 ± 0.6, R unconstrained 2 = 85.6 ± 1.6) and pronation-supination DOF ( R constrained 2 = 88.5 ± 0.9, R unconstrained 2 = 82.3 ± 1.6), but no significant difference was found in the flexion-extension DOF. The constrained control method outperformed unconstrained control in two real-time testing metrics including completion time and path efficiency. The constrained method results, however, were not significantly different than those of the resisted unconstrained method (with braces) in both off-line and real-time tests. This suggests that the quality of control using constrained and unconstrained contraction-based myoelectric schemes is not appreciably different when using comparable levels of muscle activation. |
|---|---|
| AbstractList | In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DOFs) (wrist flexion-extension, abduction-adduction, and pronation-supination) using mirrored bilateral contractions. In the resisted unconstrained experiment, some resistance to movement was provided using flexible wrist braces in order to increase the required level of muscle activation. The force, in constrained experiments, and position, in unconstrained and resisted unconstrained experiments, were measured. The three protocols were compared off-line using estimation accuracies ( R 2 ) and online using a real-time computer-based target acquisition test. The constrained control paradigm outperformed the unconstrained method in the abduction-adduction DOF ( R rm constrained 2 = 90.8 plus or minus 0.6, R rm unconstrained 2 = 85.6 plus or minus 1.6) and pronation-supination DOF ( R rm constrained 2 = 88.5 plus or minus 0.9, R rm unconstrained 2 = 82.3 plus or minus 1.6), but no significant difference was found in the flexion-extension DOF. The constrained control method outperformed unconstrained control in two real-time testing metrics including completion time and path efficiency. The constrained method results, however, were not significantly different than those of the resisted unconstrained method (with braces) in both off-line and real-time tests. This suggests that the quality of control using constrained and unconstrained contraction-based myoelectric schemes is not appreciably different when using comparable levels of muscle activation. In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DOFs) (wrist flexion-extension, abduction-adduction, and pronation-supination) using mirrored bilateral contractions. In the resisted unconstrained experiment, some resistance to movement was provided using flexible wrist braces in order to increase the required level of muscle activation. The force, in constrained experiments, and position, in unconstrained and resisted unconstrained experiments, were measured. The three protocols were compared off-line using estimation accuracies (R2) and online using a real-time computer-based target acquisition test. The constrained control paradigm outperformed the unconstrained method in the abduction-adduction DOF (R(constrained)2 = 90.8 ± 0.6, R(unconstrained)2 = 85.6 ± 1.6) and pronation-supination DOF (R(constrained)2 = 88.5 ± 0.9, R(unconstrained)2 = 82.3 ± 1.6), but no significant difference was found in the flexion-extension DOF. The constrained control method outperformed unconstrained control in two real-time testing metrics including completion time and path efficiency. The constrained method results, however, were not significantly different than those of the resisted unconstrained method (with braces) in both off-line and real-time tests. This suggests that the quality of control using constrained and unconstrained contraction-based myoelectric schemes is not appreciably different when using comparable l- vels of muscle activation.In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DOFs) (wrist flexion-extension, abduction-adduction, and pronation-supination) using mirrored bilateral contractions. In the resisted unconstrained experiment, some resistance to movement was provided using flexible wrist braces in order to increase the required level of muscle activation. The force, in constrained experiments, and position, in unconstrained and resisted unconstrained experiments, were measured. The three protocols were compared off-line using estimation accuracies (R2) and online using a real-time computer-based target acquisition test. The constrained control paradigm outperformed the unconstrained method in the abduction-adduction DOF (R(constrained)2 = 90.8 ± 0.6, R(unconstrained)2 = 85.6 ± 1.6) and pronation-supination DOF (R(constrained)2 = 88.5 ± 0.9, R(unconstrained)2 = 82.3 ± 1.6), but no significant difference was found in the flexion-extension DOF. The constrained control method outperformed unconstrained control in two real-time testing metrics including completion time and path efficiency. The constrained method results, however, were not significantly different than those of the resisted unconstrained method (with braces) in both off-line and real-time tests. This suggests that the quality of control using constrained and unconstrained contraction-based myoelectric schemes is not appreciably different when using comparable l- vels of muscle activation. In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DOFs) (wrist flexion-extension, abduction-adduction, and pronation-supination) using mirrored bilateral contractions. In the resisted unconstrained experiment, some resistance to movement was provided using flexible wrist braces in order to increase the required level of muscle activation. The force, in constrained experiments, and position, in unconstrained and resisted unconstrained experiments, were measured. The three protocols were compared off-line using estimation accuracies (R2) and online using a real-time computer-based target acquisition test. The constrained control paradigm outperformed the unconstrained method in the abduction-adduction DOF (R(constrained)2 = 90.8 ± 0.6, R(unconstrained)2 = 85.6 ± 1.6) and pronation-supination DOF (R(constrained)2 = 88.5 ± 0.9, R(unconstrained)2 = 82.3 ± 1.6), but no significant difference was found in the flexion-extension DOF. The constrained control method outperformed unconstrained control in two real-time testing metrics including completion time and path efficiency. The constrained method results, however, were not significantly different than those of the resisted unconstrained method (with braces) in both off-line and real-time tests. This suggests that the quality of control using constrained and unconstrained contraction-based myoelectric schemes is not appreciably different when using comparable l- vels of muscle activation. In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DOFs) (wrist flexion-extension, abduction-adduction, and pronation-supination) using mirrored bilateral contractions. In the resisted unconstrained experiment, some resistance to movement was provided using flexible wrist braces in order to increase the required level of muscle activation. The force, in constrained experiments, and position, in unconstrained and resisted unconstrained experiments, were measured. The three protocols were compared off-line using estimation accuracies [Formula Omitted] and online using a real-time computer-based target acquisition test. The constrained control paradigm outperformed the unconstrained method in the abduction-adduction DOF [Formula Omitted] = 90.8 ± 0.6, [Formula Omitted] = 85.6 ± 1.6) and pronation-supination DOF ([Formula Omitted] = 88.5 ± 0.9, [Formula Omitted] = 82.3 ± 1.6), but no significant difference was found in the flexion-extension DOF. The constrained control method outperformed unconstrained control in two real-time testing metrics including completion time and path efficiency. The constrained method results, however, were not significantly different than those of the resisted unconstrained method (with braces) in both off-line and real-time tests. This suggests that the quality of control using constrained and unconstrained contraction-based myoelectric schemes is not appreciably different when using comparable levels of muscle activation. In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DOFs) (wrist flexion-extension, abduction-adduction, and pronation-supination) using mirrored bilateral contractions. In the resisted unconstrained experiment, some resistance to movement was provided using flexible wrist braces in order to increase the required level of muscle activation. The force, in constrained experiments, and position, in unconstrained and resisted unconstrained experiments, were measured. The three protocols were compared off-line using estimation accuracies (R 2 ) and online using a real-time computer-based target acquisition test. The constrained control paradigm outperformed the unconstrained method in the abduction-adduction DOF (R constrained 2 = 90.8 ± 0.6, R unconstrained 2 = 85.6 ± 1.6) and pronation-supination DOF ( R constrained 2 = 88.5 ± 0.9, R unconstrained 2 = 82.3 ± 1.6), but no significant difference was found in the flexion-extension DOF. The constrained control method outperformed unconstrained control in two real-time testing metrics including completion time and path efficiency. The constrained method results, however, were not significantly different than those of the resisted unconstrained method (with braces) in both off-line and real-time tests. This suggests that the quality of control using constrained and unconstrained contraction-based myoelectric schemes is not appreciably different when using comparable levels of muscle activation. |
| Author | Englehart, Kevin B. Kamavuako, Ernest Nlandu Ameri, Ali Scheme, Erik J. Parker, Philip A. |
| Author_xml | – sequence: 1 givenname: Ali surname: Ameri fullname: Ameri, Ali email: ali.ameri@unb.ca organization: Department of Electrical and Computer Engineering, Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada – sequence: 2 givenname: Erik J. surname: Scheme fullname: Scheme, Erik J. email: escheme@unb.ca organization: Department of Electrical and Computer Engineering, Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada – sequence: 3 givenname: Ernest Nlandu surname: Kamavuako fullname: Kamavuako, Ernest Nlandu email: enk@hst.aau.dk organization: Department of Health Science and Technology, Center for Sensory-Motor Interaction, Aalborg University, Aalborg , Denmark – sequence: 4 givenname: Kevin B. surname: Englehart fullname: Englehart, Kevin B. email: kengleha@unb.ca organization: Department of Electrical and Computer Engineering, Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada – sequence: 5 givenname: Philip A. surname: Parker fullname: Parker, Philip A. email: pap@unb.ca organization: Department of Electrical and Computer Engineering, Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24058007$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkU1LHTEUhkOx1KvtDyiCDLhx4dzmc5Is9aK2oFTa67KE3MwZicwkmsws_PdmuNcuXJSuwoHnOeS87wHaCzEAQl8JXhKC9bf1xe3lkmLClpQqIrT4gBZECFVTwcgeWmBMVK2p5vvoIOfHMnLFm09on3IsFMZygf78AtvXaz_AWfXbD1M_2gBxytXtS4Qe3Ji8q1YxjCn21X324aG6islBZUNb3cXsRx9DfWEztNU6WR9m4s4m2_qHIX9GHzvbZ_iyew_R_dXlevW9vvl5_WN1flM7puhYc7VRWDEA3pSfYSGlA9pSupFd17UEN1xS0LZTkoHmREurgDPdSu1axjvCDtHpdu9Tis8T5NEMPjvo--0xhghKGCum-A-UUEaVUk1BT96hj3FKoRxSKMwFF4LLQh3vqGkzQGuekh9sejFvGRdAbgGXYs4JOuP8aOfcxhJYbwg2c5tmbtPMbZpdm8Uk78y35f9yjraOB4C_fNNgrIVkr_Ojp8I |
| CODEN | IEBEAX |
| CitedBy_id | crossref_primary_10_1088_1741_2560_11_5_056008 crossref_primary_10_1371_journal_pone_0186318 crossref_primary_10_1002_tee_22920 crossref_primary_10_1007_s00221_018_5441_x crossref_primary_10_1016_j_bspc_2023_105044 crossref_primary_10_1016_j_jelekin_2017_03_004 crossref_primary_10_3389_fnins_2017_00480 crossref_primary_10_1007_s00422_014_0635_1 crossref_primary_10_1007_s00521_021_05743_y crossref_primary_10_3233_THC_220283 crossref_primary_10_1109_TNSRE_2015_2401134 crossref_primary_10_3389_fnbot_2016_00003 crossref_primary_10_1007_s11517_018_1940_y crossref_primary_10_1007_s12206_017_1154_5 crossref_primary_10_1142_S0219519417501202 crossref_primary_10_1186_s12984_024_01529_0 crossref_primary_10_1109_TNSRE_2014_2323576 crossref_primary_10_1109_TNSRE_2016_2639443 crossref_primary_10_1016_j_bspc_2022_104088 crossref_primary_10_1088_1741_2552_ab673f crossref_primary_10_1016_j_bspc_2021_103134 crossref_primary_10_1088_1361_6501_ab0eae crossref_primary_10_1016_j_bspc_2021_103005 crossref_primary_10_3390_app10082892 crossref_primary_10_1088_1741_2552_aa61bc crossref_primary_10_1109_TBME_2019_2900415 crossref_primary_10_1109_TNSRE_2022_3166800 crossref_primary_10_3390_technologies5040064 crossref_primary_10_1007_s11517_018_1807_2 crossref_primary_10_1109_TIE_2021_3097666 crossref_primary_10_1007_s00521_024_10175_5 crossref_primary_10_1109_TNSRE_2015_2501979 crossref_primary_10_1371_journal_pone_0203835 crossref_primary_10_1109_THMS_2019_2925191 crossref_primary_10_1109_TNSRE_2019_2894464 crossref_primary_10_1016_j_bspc_2014_03_006 crossref_primary_10_1016_j_bspc_2023_104602 crossref_primary_10_3389_fbioe_2017_00003 crossref_primary_10_1177_0954411917705408 crossref_primary_10_1063_1_4932556 crossref_primary_10_1109_JBHI_2023_3238966 crossref_primary_10_1088_1741_2560_11_5_051001 crossref_primary_10_1016_j_bspc_2024_106162 crossref_primary_10_1016_j_jelekin_2017_06_001 crossref_primary_10_1109_TNSRE_2015_2481461 crossref_primary_10_1109_TNSRE_2020_3022587 crossref_primary_10_3390_electronics8111244 crossref_primary_10_1088_1741_2552_aad727 crossref_primary_10_3390_s24103101 crossref_primary_10_1016_j_compbiomed_2022_105359 crossref_primary_10_1109_TNSRE_2020_3038322 crossref_primary_10_1088_1741_2552_ab0e2e crossref_primary_10_1109_TNSRE_2014_2305111 crossref_primary_10_1080_03772063_2017_1381047 crossref_primary_10_1016_j_bspc_2019_04_027 crossref_primary_10_1088_1741_2560_13_2_026012 |
| Cites_doi | 10.1016/j.jphysparis.2009.08.008 10.1109/TNSRE.2009.2039590 10.1109/TBME.2007.909536 10.1097/JPO.0b013e318217a30c 10.1007/BF02476154 10.1037/h0055392 10.1016/j.bspc.2007.09.002 10.1109/IEMBS.2009.5332745 10.1109/TNSRE.2012.2226189 10.1016/0141-5425(82)90021-8 10.1109/TNSRE.2011.2178864 10.1080/10400435.1990.10132142 10.1016/S1350-4533(99)00066-1 10.1109/TNSRE.2008.2006216 10.1080/03091900512331332546 10.1109/TNSRE.2011.2178039 10.1007/s10439-011-0438-7 10.2170/jjphysiol.47.487 10.1016/j.bspc.2012.05.002 10.2174/1874230001206010005 10.1682/JRRD.2010.08.0149 10.1109/BioRob.2012.6290709 10.1109/TNSRE.2012.2196711 10.1109/TBME.2008.2007967 10.1586/erd.11.23 10.1109/10.204774 10.1109/TITB.2010.2040832 10.1109/TBME.2010.2068298 10.1109/EMBC.2012.6346186 10.1109/TNSRE.2011.2108667 10.1109/TBME.2005.856295 10.1109/IEMBS.2010.5627622 10.1097/JPO.0b013e318289950b |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Feb 2014 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Feb 2014 |
| DBID | 97E RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 |
| DOI | 10.1109/TBME.2013.2281595 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Civil Engineering Abstracts Aluminium Industry Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Ceramic Abstracts Materials Business File METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Aerospace Database Engineered Materials Abstracts Biotechnology Research Abstracts Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic |
| DatabaseTitleList | Engineering Research Database MEDLINE - Academic MEDLINE Materials Research Database |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Engineering |
| EISSN | 1558-2531 |
| EndPage | 287 |
| ExternalDocumentID | 3237819531 24058007 10_1109_TBME_2013_2281595 6600957 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: Natural Sciences and Engineering Research Council of Canada (NSERC) grantid: 217354-10; A4445-2004 |
| GroupedDBID | --- -~X .55 .DC .GJ 0R~ 29I 4.4 53G 5GY 5RE 5VS 6IF 6IK 6IL 6IN 85S 97E AAJGR AARMG AASAJ AAWTH AAYJJ ABAZT ABJNI ABQJQ ABVLG ACGFO ACGFS ACIWK ACKIV ACNCT ACPRK ADZIZ AENEX AETIX AFFNX AFRAH AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CHZPO CS3 DU5 EBS EJD F5P HZ~ H~9 IAAWW IBMZZ ICLAB IDIHD IEGSK IFIPE IFJZH IPLJI JAVBF LAI MS~ O9- OCL P2P RIA RIE RIL RNS TAE TN5 VH1 VJK X7M ZGI ZXP AAYXX CITATION CGR CUY CVF ECM EIF NPM RIG 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 |
| ID | FETCH-LOGICAL-c382t-48b8083ee462400577ce2d22b7fffd106472e9af873e94197a8e439d79cd34f13 |
| IEDL.DBID | RIE |
| ISSN | 0018-9294 1558-2531 |
| IngestDate | Thu Oct 02 10:47:19 EDT 2025 Tue Oct 07 07:59:58 EDT 2025 Mon Jun 30 08:33:44 EDT 2025 Thu Apr 03 06:49:29 EDT 2025 Wed Oct 01 02:57:18 EDT 2025 Thu Apr 24 23:06:32 EDT 2025 Wed Aug 27 02:52:56 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c382t-48b8083ee462400577ce2d22b7fffd106472e9af873e94197a8e439d79cd34f13 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Conference-1 ObjectType-Feature-3 content type line 23 SourceType-Conference Papers & Proceedings-2 |
| PMID | 24058007 |
| PQID | 1504545547 |
| PQPubID | 85474 |
| PageCount | 9 |
| ParticipantIDs | proquest_journals_1504545547 crossref_citationtrail_10_1109_TBME_2013_2281595 crossref_primary_10_1109_TBME_2013_2281595 proquest_miscellaneous_1521334195 ieee_primary_6600957 proquest_miscellaneous_1512328886 pubmed_primary_24058007 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2014-Feb. 2014-2-00 2014-Feb 20140201 |
| PublicationDateYYYYMMDD | 2014-02-01 |
| PublicationDate_xml | – month: 02 year: 2014 text: 2014-Feb. |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: New York |
| PublicationTitle | IEEE transactions on biomedical engineering |
| PublicationTitleAbbrev | TBME |
| PublicationTitleAlternate | IEEE Trans Biomed Eng |
| PublicationYear | 2014 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref35 ref13 ref34 ref12 ref15 ref36 ref14 ref31 ref30 childress (ref8) 1969 ref33 ref11 ref32 ref10 ref2 ref1 ref17 ref16 ref18 kamavuako (ref19) 0 jiang (ref29) 2012; 9 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref7 ref9 ref4 ref3 ref6 ref5 |
| References_xml | – ident: ref24 doi: 10.1016/j.jphysparis.2009.08.008 – ident: ref14 doi: 10.1109/TNSRE.2009.2039590 – year: 0 ident: ref19 article-title: Surface versus untargeted intramuscular EMG based classification of simultaneous and unconstrainedally changing movements publication-title: IEEE Trans Neural Syst Rehabil Eng – ident: ref2 doi: 10.1109/TBME.2007.909536 – ident: ref4 doi: 10.1097/JPO.0b013e318217a30c – ident: ref7 doi: 10.1007/BF02476154 – ident: ref35 doi: 10.1037/h0055392 – ident: ref13 doi: 10.1016/j.bspc.2007.09.002 – ident: ref36 doi: 10.1109/IEMBS.2009.5332745 – ident: ref34 doi: 10.1109/TNSRE.2012.2226189 – ident: ref15 doi: 10.1016/0141-5425(82)90021-8 – ident: ref3 doi: 10.1109/TNSRE.2011.2178864 – ident: ref9 doi: 10.1080/10400435.1990.10132142 – ident: ref11 doi: 10.1016/S1350-4533(99)00066-1 – ident: ref33 doi: 10.1109/TNSRE.2008.2006216 – ident: ref26 doi: 10.1080/03091900512331332546 – ident: ref28 doi: 10.1109/TNSRE.2011.2178039 – ident: ref25 doi: 10.1007/s10439-011-0438-7 – ident: ref21 doi: 10.2170/jjphysiol.47.487 – ident: ref31 doi: 10.1016/j.bspc.2012.05.002 – ident: ref5 doi: 10.2174/1874230001206010005 – ident: ref32 doi: 10.1682/JRRD.2010.08.0149 – ident: ref18 doi: 10.1109/BioRob.2012.6290709 – ident: ref16 doi: 10.1109/TNSRE.2012.2196711 – ident: ref22 doi: 10.1109/TBME.2008.2007967 – ident: ref6 doi: 10.1586/erd.11.23 – ident: ref10 doi: 10.1109/10.204774 – year: 1969 ident: ref8 article-title: A myoelectric three state controller using rate sensitivity publication-title: Int Conf Math Biology Ecology – ident: ref20 doi: 10.1109/TITB.2010.2040832 – ident: ref23 doi: 10.1109/TBME.2010.2068298 – ident: ref30 doi: 10.1109/EMBC.2012.6346186 – ident: ref1 doi: 10.1109/TNSRE.2011.2108667 – volume: 9 start-page: epub year: 2012 ident: ref29 article-title: EMG-based simultaneous and proportional estimation of wrist/hand unconstraineds in uni-lateral trans-radial amputees publication-title: J Neuro Eng Rehabil – ident: ref12 doi: 10.1109/TBME.2005.856295 – ident: ref27 doi: 10.1109/IEMBS.2010.5627622 – ident: ref17 doi: 10.1097/JPO.0b013e318289950b |
| SSID | ssj0014846 |
| Score | 2.3848839 |
| Snippet | In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 279 |
| SubjectTerms | Adult Biomedical Engineering - instrumentation Constrained contractions electromyogram Electromyography Electromyography - instrumentation Electromyography - methods Force Forearm - physiology Humans Joints Man-Machine Systems Muscle, Skeletal - physiology myoelectric control powered prostheses Prostheses and Implants Protocols Range of Motion, Articular Real-time systems Signal Processing, Computer-Assisted Training unconstrained contractions Wrist Young Adult |
| Title | Real-Time, Simultaneous Myoelectric Control Using Force and Position-Based Training Paradigms |
| URI | https://ieeexplore.ieee.org/document/6600957 https://www.ncbi.nlm.nih.gov/pubmed/24058007 https://www.proquest.com/docview/1504545547 https://www.proquest.com/docview/1512328886 https://www.proquest.com/docview/1521334195 |
| Volume | 61 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-2531 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014846 issn: 0018-9294 databaseCode: RIE dateStart: 19640101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB61PSA4FGh5LBRkJE6o2SaO48eRVl1VSEEVbKVeUOTYY7QCkmofB_j12LE3AgQVt0h25EnGo_ns-WYG4LWURlmWm8x5X52xsrVZq7XLrNNYta0TariHrN_ziyv27rq63oHjMRcGEQfyGU7D4xDLt73ZhKuyE84DIhC7sCskj7laY8SAyZiUkxfegKliKYJZ5OpkflqfBxJXOaVUevcdutV4R1Z5rCR-c0dDf5V_Q83B5czuQ70VNjJNvkw363ZqfvxRx_F_v-YB7CfsSd7GzfIQdrA7gHu_VCQ8gDt1irUfwqcPHkNmIUXkmHxcBOKh7rDfrEj9vY_NcxaGnEWmOxmYB2TWLw0S3Vlymbhg2an3kpbMUyMKcqmX2i4-f1s9gqvZ-fzsIkvNGDJTSrrOmGylh2uIjAfaaSWEQWopbYVzzhYhZ5Wi0k6KEhUrlNASPdixQhlbMleUj2Gv6zt8CkQJk2PuT07Kcaa5VlzkGnWoTO-QOzOBfKuTxqRK5aFhxtdmOLHkqgkabYJGm6TRCbwZX7mJZTpum3wYtDFOTIqYwNFW8U0y5FXjhWIeZFbMD78ah70JhrhK_O1NAE0llVLy2-bQogy18_zqT-KmGtff7sVnf5frOdz10rNIFT-CvfVygy88Elq3LwcT-Akj6wEY |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB6VIvE48GgLLBQwEifUbBPHie0jrbpaoKkq2Eq9oMixx2gFJGgfB_j12LE3AgQVt0h25Md4NJ8938wAvBRCS8NSnVhnqxOWNyZplLKJsQqLprFc9u-Q1Vk5vWBvL4vLLTgYYmEQsSef4dh_9r580-m1fyo7LEuPCPg1uF4wxooQrTX4DJgIYTlp5lSYShZ9mFkqD2dH1YmnceVjSoUz4L5ejTNlhUNL_DeD1FdY-TfY7I3O5C5Um-kGrsnn8XrVjPWPPzI5_u967sGdiD7J63Bc7sMWtjtw-5echDtwo4re9l34-N6hyMQHiRyQD3NPPVQtduslqb53oXzOXJPjwHUnPfeATLqFRqJaQ84jGyw5cnbSkFksRUHO1UKZ-aevyz24mJzMjqdJLMeQ6FzQVcJEIxxgQ2SlJ54WnGukhtKGW2tN5qNWKUplBc9RskxyJdDBHcOlNjmzWf4AttuuxUdAJNcppu7uJG3JVKlkyVOFyuemt1haPYJ0I5Nax1zlvmTGl7q_s6Sy9hKtvUTrKNERvBp--RYSdVzVeddLY-gYBTGC_Y3g66jKy9pNijmYWTDX_GJodkroPSth22sPm3IqhCiv6kOz3GfPc6M_DIdqGH9zFh__fV7P4eZ0Vp3Wp2_O3j2BW24lLBDH92F7tVjjU4eLVs2zXh1-Ar72BGU |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Real-Time%2C+Simultaneous+Myoelectric+Control+Using+Force+and+Position-Based+Training+Paradigms&rft.jtitle=IEEE+transactions+on+biomedical+engineering&rft.au=Ameri%2C+Ali&rft.au=Scheme%2C+Erik+J.&rft.au=Kamavuako%2C+Ernest+Nlandu&rft.au=Englehart%2C+Kevin+B.&rft.date=2014-02-01&rft.issn=0018-9294&rft.eissn=1558-2531&rft.volume=61&rft.issue=2&rft.spage=279&rft.epage=287&rft_id=info:doi/10.1109%2FTBME.2013.2281595&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TBME_2013_2281595 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9294&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9294&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9294&client=summon |