Decoding Muscle Force From Motor Unit Firings Using Encoder-Decoder Networks
Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and physiologically meaningful way. However, there are great challenges due to the difficulty in cross-trial MU tracking and unavoidable loss of...
Saved in:
| Published in: | IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 2484 - 2495 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1534-4320, 1558-0210, 1558-0210 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and physiologically meaningful way. However, there are great challenges due to the difficulty in cross-trial MU tracking and unavoidable loss of partial MU information resulting from incomplete decomposition. In light of these issues, this study presents a novel framework for interpreting MU activities and applies it to decode muscle force. The resulting MUs were clustered and classified into different categories by characterizing their spatially distributed firing waveforms. The process served as a general MU tracking method. On this basis, after transferring the MU firing trains to twitch force trains by a twitch force model, a deep network was designed to predict the normalized force. In addition, MU category distribution was examined to calibrate the actual force level, while functions of some unavailable MUs were compensated. To investigate the effectiveness of this framework, high-density sEMG signals were recorded using an <inline-formula> <tex-math notation="LaTeX">8\times8 </tex-math></inline-formula> electrode array from the abductor pollicis brevis muscles of eight subjects, while thumb abduction force was measured. The proposed method outperformed three common methods (<inline-formula> <tex-math notation="LaTeX">{p} < {0.001} </tex-math></inline-formula>) yielding the lowest root mean square deviation of 6.68% ± 1.29% and the highest fitness (<inline-formula> <tex-math notation="LaTeX">{R}^{{2}} </tex-math></inline-formula>) of 0.94 ± 0.04 between the predicted force and the actual force. This study offers a valuable, computational solution for interpreting individual MU activities, and its effectiveness was confirmed in muscle force estimation. |
|---|---|
| AbstractList | Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and physiologically meaningful way. However, there are great challenges due to the difficulty in cross-trial MU tracking and unavoidable loss of partial MU information resulting from incomplete decomposition. In light of these issues, this study presents a novel framework for interpreting MU activities and applies it to decode muscle force. The resulting MUs were clustered and classified into different categories by characterizing their spatially distributed firing waveforms. The process served as a general MU tracking method. On this basis, after transferring the MU firing trains to twitch force trains by a twitch force model, a deep network was designed to predict the normalized force. In addition, MU category distribution was examined to calibrate the actual force level, while functions of some unavailable MUs were compensated. To investigate the effectiveness of this framework, high-density sEMG signals were recorded using an <inline-formula> <tex-math notation="LaTeX">8\times8 </tex-math></inline-formula> electrode array from the abductor pollicis brevis muscles of eight subjects, while thumb abduction force was measured. The proposed method outperformed three common methods (<inline-formula> <tex-math notation="LaTeX">{p} < {0.001} </tex-math></inline-formula>) yielding the lowest root mean square deviation of 6.68% ± 1.29% and the highest fitness (<inline-formula> <tex-math notation="LaTeX">{R}^{{2}} </tex-math></inline-formula>) of 0.94 ± 0.04 between the predicted force and the actual force. This study offers a valuable, computational solution for interpreting individual MU activities, and its effectiveness was confirmed in muscle force estimation. Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and physiologically meaningful way. However, there are great challenges due to the difficulty in cross-trial MU tracking and unavoidable loss of partial MU information resulting from incomplete decomposition. In light of these issues, this study presents a novel framework for interpreting MU activities and applies it to decode muscle force. The resulting MUs were clustered and classified into different categories by characterizing their spatially distributed firing waveforms. The process served as a general MU tracking method. On this basis, after transferring the MU firing trains to twitch force trains by a twitch force model, a deep network was designed to predict the normalized force. In addition, MU category distribution was examined to calibrate the actual force level, while functions of some unavailable MUs were compensated. To investigate the effectiveness of this framework, high-density sEMG signals were recorded using an [Formula Omitted] electrode array from the abductor pollicis brevis muscles of eight subjects, while thumb abduction force was measured. The proposed method outperformed three common methods ([Formula Omitted]) yielding the lowest root mean square deviation of 6.68% ± 1.29% and the highest fitness ([Formula Omitted]) of 0.94 ± 0.04 between the predicted force and the actual force. This study offers a valuable, computational solution for interpreting individual MU activities, and its effectiveness was confirmed in muscle force estimation. Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and physiologically meaningful way. However, there are great challenges due to the difficulty in cross-trial MU tracking and unavoidable loss of partial MU information resulting from incomplete decomposition. In light of these issues, this study presents a novel framework for interpreting MU activities and applies it to decode muscle force. The resulting MUs were clustered and classified into different categories by characterizing their spatially distributed firing waveforms. The process served as a general MU tracking method. On this basis, after transferring the MU firing trains to twitch force trains by a twitch force model, a deep network was designed to predict the normalized force. In addition, MU category distribution was examined to calibrate the actual force level, while functions of some unavailable MUs were compensated. To investigate the effectiveness of this framework, high-density sEMG signals were recorded using an 8×8 electrode array from the abductor pollicis brevis muscles of eight subjects, while thumb abduction force was measured. The proposed method outperformed three common methods ( ) yielding the lowest root mean square deviation of 6.68% ± 1.29% and the highest fitness ( R ) of 0.94 ± 0.04 between the predicted force and the actual force. This study offers a valuable, computational solution for interpreting individual MU activities, and its effectiveness was confirmed in muscle force estimation. Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and physiologically meaningful way. However, there are great challenges due to the difficulty in cross-trial MU tracking and unavoidable loss of partial MU information resulting from incomplete decomposition. In light of these issues, this study presents a novel framework for interpreting MU activities and applies it to decode muscle force. The resulting MUs were clustered and classified into different categories by characterizing their spatially distributed firing waveforms. The process served as a general MU tracking method. On this basis, after transferring the MU firing trains to twitch force trains by a twitch force model, a deep network was designed to predict the normalized force. In addition, MU category distribution was examined to calibrate the actual force level, while functions of some unavailable MUs were compensated. To investigate the effectiveness of this framework, high-density sEMG signals were recorded using an 8×8 electrode array from the abductor pollicis brevis muscles of eight subjects, while thumb abduction force was measured. The proposed method outperformed three common methods ( ) yielding the lowest root mean square deviation of 6.68% ± 1.29% and the highest fitness ( R2 ) of 0.94 ± 0.04 between the predicted force and the actual force. This study offers a valuable, computational solution for interpreting individual MU activities, and its effectiveness was confirmed in muscle force estimation.Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and physiologically meaningful way. However, there are great challenges due to the difficulty in cross-trial MU tracking and unavoidable loss of partial MU information resulting from incomplete decomposition. In light of these issues, this study presents a novel framework for interpreting MU activities and applies it to decode muscle force. The resulting MUs were clustered and classified into different categories by characterizing their spatially distributed firing waveforms. The process served as a general MU tracking method. On this basis, after transferring the MU firing trains to twitch force trains by a twitch force model, a deep network was designed to predict the normalized force. In addition, MU category distribution was examined to calibrate the actual force level, while functions of some unavailable MUs were compensated. To investigate the effectiveness of this framework, high-density sEMG signals were recorded using an 8×8 electrode array from the abductor pollicis brevis muscles of eight subjects, while thumb abduction force was measured. The proposed method outperformed three common methods ( ) yielding the lowest root mean square deviation of 6.68% ± 1.29% and the highest fitness ( R2 ) of 0.94 ± 0.04 between the predicted force and the actual force. This study offers a valuable, computational solution for interpreting individual MU activities, and its effectiveness was confirmed in muscle force estimation. |
| Author | Chen, Maoqi Tang, Xiao Chen, Xun Zhang, Xu Chen, Xiang |
| Author_xml | – sequence: 1 givenname: Xiao orcidid: 0000-0002-3348-836X surname: Tang fullname: Tang, Xiao organization: School of Information Science and Technology, University of Science and Technology of China, Hefei, China – sequence: 2 givenname: Xu orcidid: 0000-0002-1533-4340 surname: Zhang fullname: Zhang, Xu email: xuzhang90@ustc.edu.cn organization: School of Information Science and Technology, University of Science and Technology of China, Hefei, China – sequence: 3 givenname: Maoqi surname: Chen fullname: Chen, Maoqi organization: Institute of Rehabilitation Engineering, University of Health and Rehabilitation Sciences, Qingdao, China – sequence: 4 givenname: Xiang orcidid: 0000-0001-8259-4815 surname: Chen fullname: Chen, Xiang organization: School of Information Science and Technology, University of Science and Technology of China, Hefei, China – sequence: 5 givenname: Xun orcidid: 0000-0002-4922-8116 surname: Chen fullname: Chen, Xun organization: School of Information Science and Technology, University of Science and Technology of China, Hefei, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34748497$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kc1LwzAchoMofkz_AQUpePHSmY-mTY8yNxW2CerOIU1_lcy20aRF_O9N3fTgwUsSwvP8SN73CO22tgWETgkeE4Lzq-fl0-N0TDElY0ZomnG6gw4J5yIOV3h3OLMkThjFB-jI-zXGJEt5to8OWJIlIsmzQzS_AW1L075Ei97rGqKZdTqszjbRwnbWRavWdNHMuMD4aOUHdNoGB1z87YKLltB9WPfqj9FepWoPJ9t9hFaz6fPkLp4_3N5PruexZjnvYqKo5gUVigJnXCtciCyBCgpIFa-4qESRKiEIK0pBsOCMClqKBOeMp0WeUTZCl5u5b86-9-A72Rivoa5VC7b3kvKcc55kmAT04g-6tr1rw-skTUkIBIccAnW-pfqigVK-OdMo9yl_cgqA2ADaWe8dVFKbTnXGtp1TppYEy6ES-V2JHCqR20qCSv-oP9P_lc42kgGAXyFPcSrC_78A3cyUrQ |
| CODEN | ITNSB3 |
| CitedBy_id | crossref_primary_10_1109_TNSRE_2024_3477607 crossref_primary_10_1109_TIM_2022_3227604 crossref_primary_10_1016_j_bspc_2024_106769 crossref_primary_10_1186_s12984_024_01345_6 crossref_primary_10_1109_TBME_2023_3294016 crossref_primary_10_1109_TNSRE_2024_3364716 crossref_primary_10_1109_TBME_2023_3331498 crossref_primary_10_1109_TNSRE_2024_3375320 |
| Cites_doi | 10.1038/s41551-016-0025 10.1109/TBME.2012.2191551 10.1016/j.jelekin.2006.09.005 10.1113/jphysiol.1973.sp010087 10.1109/TBME.2019.2952890 10.1109/TNSRE.2017.2759664 10.1016/j.bspc.2019.02.011 10.1109/TNSRE.2015.2412038 10.1016/j.clinph.2008.08.005 10.1109/CVPR.2017.195 10.1049/cp:19991218 10.1136/thx.50.11.1131 10.1080/01621459.1990.10476211 10.1016/j.bspc.2019.101637 10.1088/1741-2552/aa63ba 10.1088/1757-899X/336/1/012017 10.1109/10.764949 10.1007/s10439-007-9329-3 10.1088/1741-2552/ab4d05 10.1109/TBME.2018.2834555 10.1109/TNSRE.2018.2864317 10.1109/TFUZZ.2004.840133 10.1007/978-3-540-73044-6_29 10.1162/neco.1997.9.8.1735 10.1016/j.clinph.2008.10.160 10.1113/JP273662 10.1109/72.761722 10.1152/jn.00301.2012 10.1109/TBME.1980.326653 10.1109/TBME.2020.3006508 10.1109/TBME.2006.889190 10.1088/1741-2552/aaf4c3 10.1109/TNNLS.2013.2293795 10.1109/JBHI.2016.2626399 10.1109/TSMCA.2011.2116004 10.1002/mus.20265 10.1109/TBME.2020.2989311 10.1152/jn.1993.70.6.2470 10.1155/2016/3489540 10.1249/00005768-198023000-00014 10.1145/2623330.2623612 10.1113/JP276153 10.1007/s10898-007-9162-0 10.1109/ISIE.2006.296086 10.1109/3477.764879 10.3389/fneur.2018.00187 10.1109/TNSRE.2020.3030931 10.3389/fnbot.2019.00007 10.1109/TNSRE.2017.2766360 10.1109/TBME.2013.2238939 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D NAPCQ P64 7X8 |
| DOI | 10.1109/TNSRE.2021.3126752 |
| DatabaseName | IEEE Xplore (IEEE) IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore 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 Neurosciences 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 Nursing & Allied Health Premium 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 Neurosciences Abstracts Materials Business File METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Aerospace Database Nursing & Allied Health Premium 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 | Materials Research Database MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Occupational Therapy & Rehabilitation |
| EISSN | 1558-0210 |
| EndPage | 2495 |
| ExternalDocumentID | 34748497 10_1109_TNSRE_2021_3126752 9606872 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 61771444 funderid: 10.13039/501100001809 – fundername: Guangzhou Science and Technology Program grantid: 201704030039 |
| GroupedDBID | --- -~X 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E AAFWJ AAJGR AASAJ AAWTH ABAZT ABVLG ACGFO ACGFS ACIWK ACPRK AENEX AETIX AFPKN AFRAH AGSQL AIBXA ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD ESBDL F5P GROUPED_DOAJ HZ~ H~9 IFIPE IPLJI JAVBF LAI M43 O9- OCL OK1 P2P RIA RIE RNS AAYXX CITATION CGR CUY CVF ECM EIF NPM RIG 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D NAPCQ P64 7X8 |
| ID | FETCH-LOGICAL-c395t-1a2c5b28a2e535ca0b874efebe6a5f58f8b6a8813bd810853282d8409356b9723 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 9 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000730473200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1534-4320 1558-0210 |
| IngestDate | Thu Oct 02 10:21:37 EDT 2025 Sun Nov 30 04:37:30 EST 2025 Wed Feb 19 02:26:47 EST 2025 Tue Nov 18 22:07:01 EST 2025 Sat Nov 29 01:47:13 EST 2025 Wed Aug 27 05:07:53 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://creativecommons.org/licenses/by-nc-nd/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c395t-1a2c5b28a2e535ca0b874efebe6a5f58f8b6a8813bd810853282d8409356b9723 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-4922-8116 0000-0002-3348-836X 0000-0001-8259-4815 0000-0002-1533-4340 |
| OpenAccessLink | https://ieeexplore.ieee.org/document/9606872 |
| PMID | 34748497 |
| PQID | 2610170849 |
| PQPubID | 85423 |
| PageCount | 12 |
| ParticipantIDs | crossref_citationtrail_10_1109_TNSRE_2021_3126752 proquest_miscellaneous_2595554701 pubmed_primary_34748497 ieee_primary_9606872 proquest_journals_2610170849 crossref_primary_10_1109_TNSRE_2021_3126752 |
| PublicationCentury | 2000 |
| PublicationDate | 20210000 2021-00-00 20210101 |
| PublicationDateYYYYMMDD | 2021-01-01 |
| PublicationDate_xml | – year: 2021 text: 20210000 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: New York |
| PublicationTitle | IEEE transactions on neural systems and rehabilitation engineering |
| PublicationTitleAbbrev | TNSRE |
| PublicationTitleAlternate | IEEE Trans Neural Syst Rehabil Eng |
| PublicationYear | 2021 |
| 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 | ref13 ref12 ref15 ref14 ref53 ref52 ref11 ref10 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 bholowalia (ref29) 2014; 105 ref49 ref8 howard (ref34) 2017 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref37 ref36 ref31 ref30 ref33 ref32 arthur (ref26) 2007 ref2 ref1 ref39 ref38 ref24 ref23 ref25 ref20 ref22 ref21 ref28 ref27 |
| References_xml | – start-page: 1027 year: 2007 ident: ref26 article-title: K-means plus plus: The advantages of careful seeding publication-title: Proc 18th Annu ACM-SIAM Symp Discrete Algorithms – ident: ref22 doi: 10.1038/s41551-016-0025 – ident: ref6 doi: 10.1109/TBME.2012.2191551 – ident: ref9 doi: 10.1016/j.jelekin.2006.09.005 – ident: ref31 doi: 10.1113/jphysiol.1973.sp010087 – ident: ref50 doi: 10.1109/TBME.2019.2952890 – ident: ref16 doi: 10.1109/TNSRE.2017.2759664 – ident: ref4 doi: 10.1016/j.bspc.2019.02.011 – ident: ref17 doi: 10.1109/TNSRE.2015.2412038 – ident: ref48 doi: 10.1016/j.clinph.2008.08.005 – ident: ref35 doi: 10.1109/CVPR.2017.195 – ident: ref37 doi: 10.1049/cp:19991218 – ident: ref8 doi: 10.1136/thx.50.11.1131 – ident: ref41 doi: 10.1080/01621459.1990.10476211 – year: 2017 ident: ref34 article-title: MobileNets: Efficient convolutional neural networks for mobile vision applications publication-title: arXiv 1704 04861 – ident: ref20 doi: 10.1016/j.bspc.2019.101637 – ident: ref12 doi: 10.1088/1741-2552/aa63ba – ident: ref30 doi: 10.1088/1757-899X/336/1/012017 – ident: ref10 doi: 10.1109/10.764949 – ident: ref33 doi: 10.1007/s10439-007-9329-3 – ident: ref44 doi: 10.1088/1741-2552/ab4d05 – ident: ref1 doi: 10.1109/TBME.2018.2834555 – ident: ref25 doi: 10.1109/TNSRE.2018.2864317 – ident: ref40 doi: 10.1109/TFUZZ.2004.840133 – ident: ref24 doi: 10.1007/978-3-540-73044-6_29 – ident: ref36 doi: 10.1162/neco.1997.9.8.1735 – ident: ref18 doi: 10.1016/j.clinph.2008.10.160 – ident: ref42 doi: 10.1113/JP273662 – ident: ref46 doi: 10.1109/72.761722 – ident: ref52 doi: 10.1152/jn.00301.2012 – ident: ref11 doi: 10.1109/TBME.1980.326653 – ident: ref47 doi: 10.1109/TBME.2020.3006508 – volume: 105 start-page: 17 year: 2014 ident: ref29 article-title: EBK-means: A clustering technique based on elbow method and k-means in WSN publication-title: Int J Comput Appl – ident: ref14 doi: 10.1109/TBME.2006.889190 – ident: ref19 doi: 10.1088/1741-2552/aaf4c3 – ident: ref27 doi: 10.1109/TNNLS.2013.2293795 – ident: ref13 doi: 10.1109/JBHI.2016.2626399 – ident: ref5 doi: 10.1109/TSMCA.2011.2116004 – ident: ref53 doi: 10.1002/mus.20265 – ident: ref45 doi: 10.1109/TBME.2020.2989311 – ident: ref32 doi: 10.1152/jn.1993.70.6.2470 – ident: ref15 doi: 10.1155/2016/3489540 – ident: ref7 doi: 10.1249/00005768-198023000-00014 – ident: ref38 doi: 10.1145/2623330.2623612 – ident: ref21 doi: 10.1113/JP276153 – ident: ref39 doi: 10.1007/s10898-007-9162-0 – ident: ref3 doi: 10.1109/ISIE.2006.296086 – ident: ref28 doi: 10.1109/3477.764879 – ident: ref23 doi: 10.3389/fneur.2018.00187 – ident: ref49 doi: 10.1109/TNSRE.2020.3030931 – ident: ref2 doi: 10.3389/fnbot.2019.00007 – ident: ref43 doi: 10.1109/TNSRE.2017.2766360 – ident: ref51 doi: 10.1109/TBME.2013.2238939 |
| SSID | ssj0017657 |
| Score | 2.38379 |
| Snippet | Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 2484 |
| SubjectTerms | Coders Computer applications Decoding Decomposition Deep learning Electrodes Electromyography EMG decomposition Encoders-Decoders Estimation Force Humans Mechanical Phenomena motor unit Movement Muscle Contraction Muscle force estimation Muscle, Skeletal Muscles neural drive information Thumb Tracking Waveforms |
| Title | Decoding Muscle Force From Motor Unit Firings Using Encoder-Decoder Networks |
| URI | https://ieeexplore.ieee.org/document/9606872 https://www.ncbi.nlm.nih.gov/pubmed/34748497 https://www.proquest.com/docview/2610170849 https://www.proquest.com/docview/2595554701 |
| Volume | 29 |
| WOSCitedRecordID | wos000730473200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1558-0210 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017657 issn: 1534-4320 databaseCode: DOA dateStart: 20210101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-0210 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017657 issn: 1534-4320 databaseCode: RIE dateStart: 20010101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwEB2VigMXoLRAoKyMRHspoY4Tx84Rtbvi0K5Qu1R7i2zHK1WiSZXs8vuZcbJRkQCplyhS7HzN2J4343kD8MmrJBPcBT5tERP-io01q1jhXGiLwnMTouc3F2o-18tl8X0HPo-5MN77sPnMf6HTEMuvGrchV9kpWdta4YT7RCnV52qNEQOVB1ZPHMBZnKWCbxNkeHG6mF9fTREKigQRqkALmUrYpBmxaBLX04P1KBRY-betGdac2YvHve1LeD7Yluxrrwx7sOPrV3D0kEeYLXoSAXbMrv6g6N6Hi3MEorSQsctNh93ZrGkdHtvmjl02iMwZmadsdkuOwI6FrQZsWlNKfBuHvr5l835TeXcAP2bTxdm3eCi1ELu0kOs4McJJK7QRXqbSGW61yvwKJZwbuZJ6pW1utE5SW2nKV0gRqVWEDVOZWypc9hp266b2b4FlRlk063LuHc4OjqoWClFJR8UhufVVBMn2h5du-Egqh_GzDHiEF2WQV0nyKgd5RXAy9rnvWTj-23qfpDG2HAQRweFWruUwULsSASQxCKEmRPBxvIxDjOImpvbNBtvIQqLVpXgSwZteH8Z7b9Xo3d-f-R6e0Zv1PptD2F23G_8Bnrpf69uunaAeL_UkuAEmQZt_Azo76y8 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwEB1VLVK5UGj5CBQwEuUCoY4TJ84RlV0VdTeq2i3qLbIdr1QJEpTs8vuZcbJRKwFSL1Gk2ImdGdvzPJ43AO9dFiWCW8-nLULCX6E2ehlmOBeaPHdce-_591lWFOr6Oj_fgk9jLIxzzh8-c5_p1vvyq8auaavsmKxtleGEuyOTRER9tNboM8hSz-uJQzgJk1jwTYgMz48XxeXFBMGgiBCjCrSRKYlNnBCPJrE93VqRfIqVf1ubftWZ7t2vvY_h0WBdsi-9OjyBLVfvw9FtJmG26GkE2Ad2cYek-wBmXxGK0lLG5usOq7Np01q8ts1PNm8QmzMyUNn0hrYCO-YPG7BJTUHxbejrupYV_bHy7ilcTSeLk9NwSLYQ2jiXqzDSwkojlBZOxtJqblSWuCXKONVyKdVSmVQrFcWmUhSxECNWqwgdxjI1lLrsGWzXTe1eAEt0ZtCwS7mzOD9YylsoRCUtpYfkxlUBRJsfXtqhk5QQ40fpEQnPSy-vkuRVDvIK4ONY51fPw_Hf0gckjbHkIIgADjdyLYeh2pUIIYlDCDUhgHfjYxxk5DnRtWvWWEbmEu2ujEcBPO_1YXz3Ro1e_v2bb2H3dDGflbNvxdkreEit7HdwDmF71a7da3hgf69uuvaN1-Y_ivjsmQ |
| 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=Decoding+Muscle+Force+From+Motor+Unit+Firings+Using+Encoder-Decoder+Networks&rft.jtitle=IEEE+transactions+on+neural+systems+and+rehabilitation+engineering&rft.au=Tang%2C+Xiao&rft.au=Zhang%2C+Xu&rft.au=Chen%2C+Maoqi&rft.au=Chen%2C+Xiang&rft.date=2021&rft.issn=1534-4320&rft.eissn=1558-0210&rft.volume=29&rft.spage=2484&rft.epage=2495&rft_id=info:doi/10.1109%2FTNSRE.2021.3126752&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TNSRE_2021_3126752 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1534-4320&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1534-4320&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1534-4320&client=summon |