Online Unsupervised Adaptation of Latent Representation for Myoelectric Control During User-Decoder Co-Adaptation
Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., because of changes in the electrode location), which m...
Uloženo v:
| Vydáno v: | IEEE transactions on neural systems and rehabilitation engineering Ročník 33; s. 1026 - 1037 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
IEEE
2025
|
| Témata: | |
| ISSN: | 1534-4320, 1558-0210, 1558-0210 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., because of changes in the electrode location), which makes interfaces based on static mapping unstable. Thus the user-decoder co-adaptation is needed during online operations. Nevertheless, current online decoder adaptation approaches present several practical challenges, such as expensive data labeling and slow convergence. Thus we introduce an unsupervised decoder adaptation method that converges rapidly. We use an autoencoder to extract motor intent representation in the latent manifold space rather than the sensor space, and further introduce an online unsupervised adaptation scheme based on Moore-Penrose Inverse, a noniterative approach suited for fast network re-training, to track the evolving manifold. A validation experiment first showed that the convergence time of the proposed adaptation scheme was reduced to about 50% of that for state-of-the-art methods. Online experiments further evaluated cursor and prosthetic hand control by the proposed myocontrol interface, where perturbations were representatively introduced by shifting the electrodes. Results showed that our scheme reached comparable improvements in robustness as supervised counterparts. Moreover, in a cup relocation test with a prosthetic hand, the completion time in the post-adaptation phase with electrode shift was comparable to that in the baseline phase without shift. These results suggest that our method effectively improves the accessibility and reliability of decoder adaptation, which has the potential to reduce the translational gap of myoelectric control interfaces by effective co-adaptation during operation. |
|---|---|
| AbstractList | Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., because of changes in the electrode location), which makes interfaces based on static mapping unstable. Thus the user-decoder co-adaptation is needed during online operations. Nevertheless, current online decoder adaptation approaches present several practical challenges, such as expensive data labeling and slow convergence. Thus we introduce an unsupervised decoder adaptation method that converges rapidly. We use an autoencoder to extract motor intent representation in the latent manifold space rather than the sensor space, and further introduce an online unsupervised adaptation scheme based on Moore-Penrose Inverse, a noniterative approach suited for fast network re-training, to track the evolving manifold. A validation experiment first showed that the convergence time of the proposed adaptation scheme was reduced to about 50% of that for state-of-the-art methods. Online experiments further evaluated cursor and prosthetic hand control by the proposed myocontrol interface, where perturbations were representatively introduced by shifting the electrodes. Results showed that our scheme reached comparable improvements in robustness as supervised counterparts. Moreover, in a cup relocation test with a prosthetic hand, the completion time in the post-adaptation phase with electrode shift was comparable to that in the baseline phase without shift. These results suggest that our method effectively improves the accessibility and reliability of decoder adaptation, which has the potential to reduce the translational gap of myoelectric control interfaces by effective co-adaptation during operation. Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., because of changes in the electrode location), which makes interfaces based on static mapping unstable. Thus the user-decoder co-adaptation is needed during online operations. Nevertheless, current online decoder adaptation approaches present several practical challenges, such as expensive data labeling and slow convergence. Thus we introduce an unsupervised decoder adaptation method that converges rapidly. We use an autoencoder to extract motor intent representation in the latent manifold space rather than the sensor space, and further introduce an online unsupervised adaptation scheme based on Moore-Penrose Inverse, a noniterative approach suited for fast network re-training, to track the evolving manifold. A validation experiment first showed that the convergence time of the proposed adaptation scheme was reduced to about 50% of that for state-of-the-art methods. Online experiments further evaluated cursor and prosthetic hand control by the proposed myocontrol interface, where perturbations were representatively introduced by shifting the electrodes. Results showed that our scheme reached comparable improvements in robustness as supervised counterparts. Moreover, in a cup relocation test with a prosthetic hand, the completion time in the post-adaptation phase with electrode shift was comparable to that in the baseline phase without shift. These results suggest that our method effectively improves the accessibility and reliability of decoder adaptation, which has the potential to reduce the translational gap of myoelectric control interfaces by effective co-adaptation during operation.Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., because of changes in the electrode location), which makes interfaces based on static mapping unstable. Thus the user-decoder co-adaptation is needed during online operations. Nevertheless, current online decoder adaptation approaches present several practical challenges, such as expensive data labeling and slow convergence. Thus we introduce an unsupervised decoder adaptation method that converges rapidly. We use an autoencoder to extract motor intent representation in the latent manifold space rather than the sensor space, and further introduce an online unsupervised adaptation scheme based on Moore-Penrose Inverse, a noniterative approach suited for fast network re-training, to track the evolving manifold. A validation experiment first showed that the convergence time of the proposed adaptation scheme was reduced to about 50% of that for state-of-the-art methods. Online experiments further evaluated cursor and prosthetic hand control by the proposed myocontrol interface, where perturbations were representatively introduced by shifting the electrodes. Results showed that our scheme reached comparable improvements in robustness as supervised counterparts. Moreover, in a cup relocation test with a prosthetic hand, the completion time in the post-adaptation phase with electrode shift was comparable to that in the baseline phase without shift. These results suggest that our method effectively improves the accessibility and reliability of decoder adaptation, which has the potential to reduce the translational gap of myoelectric control interfaces by effective co-adaptation during operation. |
| Author | Song, Aiguo Zhang, Dingguo Zeng, Hong Wei, Zhikai Hu, Xuhui Deng, Hanjie Farina, Dario |
| Author_xml | – sequence: 1 givenname: Hanjie surname: Deng fullname: Deng, Hanjie organization: State Key Laboratory of Digital Medical Engineering, Jiangsu Province Key Laboratory of Robot Sensing and Control, and the School of Instrument Science and Engineering, Southeast University, Nanjing, China – sequence: 2 givenname: Zhikai orcidid: 0009-0001-6820-9164 surname: Wei fullname: Wei, Zhikai organization: State Key Laboratory of Digital Medical Engineering, Jiangsu Province Key Laboratory of Robot Sensing and Control, and the School of Instrument Science and Engineering, Southeast University, Nanjing, China – sequence: 3 givenname: Xuhui orcidid: 0000-0001-7632-3090 surname: Hu fullname: Hu, Xuhui organization: Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China – sequence: 4 givenname: Hong orcidid: 0000-0002-4587-6263 surname: Zeng fullname: Zeng, Hong email: hzeng@seu.edu.cn organization: State Key Laboratory of Digital Medical Engineering, Jiangsu Province Key Laboratory of Robot Sensing and Control, and the School of Instrument Science and Engineering, Southeast University, Nanjing, China – sequence: 5 givenname: Aiguo orcidid: 0000-0002-1982-6780 surname: Song fullname: Song, Aiguo organization: State Key Laboratory of Digital Medical Engineering, Jiangsu Province Key Laboratory of Robot Sensing and Control, and the School of Instrument Science and Engineering, Southeast University, Nanjing, China – sequence: 6 givenname: Dingguo surname: Zhang fullname: Zhang, Dingguo organization: Department of Electronic and Electrical Engineering, University of Bath, Bath, U.K – sequence: 7 givenname: Dario orcidid: 0000-0002-7883-2697 surname: Farina fullname: Farina, Dario organization: Department of Bioengineering, Imperial College London, London, U.K |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40031560$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kU9v1DAQxS1URP_AF0AI5cglyzi2N_ax2haotFCpdM-WY08qV1k7tb2V-u3JNktBHDh5PPN7b6R5p-QoxICEvKewoBTU59sfP28uFw00YsEEF5LKV-SECiFraCgc7WvGa84aOCanOd8D0HYp2jfkmAMwKpZwQh6uw-ADVpuQdyOmR5_RVefOjMUUH0MV-2ptCoZS3eCYME_VPOhjqr4_RRzQluRttYqhpDhUF7vkw121yZjqC7TRYZpm9R_Lt-R1b4aM7w7vGdl8ubxdfavX11-vVufr2nLGS90oDlIBFYYJK4Qz0nS8McYhBUGBdx2VLVcAhqMBaZlqpr_pO9Uaya1iZ-Rq9nXR3Osx-a1JTzoar58bMd1pk4q3A2plKW2Ri27ZIXdLphhVbdMqdL1rDfST16fZa0zxYYe56K3PFofBBIy7rBltGQemlJjQjwd0123RvSz-ffIJaGbApphzwv4FoaD3uernXPU-V33IdRLJf0TWz-csyfjh_9IPs9Qj4l-7FHApJPsFJP-wbQ |
| CODEN | ITNSB3 |
| CitedBy_id | crossref_primary_10_1126_scirobotics_aea9377 |
| Cites_doi | 10.1186/s12984-018-0363-1 10.1109/TRO.2015.2395731 10.1109/TBME.2019.2952890 10.1038/s41551-020-0542-9 10.1016/j.jbiomech.2010.01.027 10.1109/TBME.2022.3194104 10.1109/TNSRE.2013.2278411 10.1109/TNSRE.2020.3038374 10.1109/TNSRE.2022.3171394 10.1007/s00521-021-06233-x 10.1109/TNSRE.2023.3347540 10.1109/TNSRE.2022.3181284 10.1109/LRA.2023.3330053 10.1038/s41593-019-0555-4 10.1109/TNSRE.2015.2401134 10.1038/nn.3265 10.1073/pnas.0500199102 10.1109/TNSRE.2019.2962189 10.1109/TNSRE.2020.3029099 10.1126/scirobotics.adp3260 10.1016/j.neuron.2014.04.048 10.1109/JSEN.2021.3098120 10.1126/scirobotics.aat3630 10.1109/TNSRE.2019.2894464 10.1016/j.neunet.2013.02.008 10.1016/S1364-6613(99)01294-2 10.1186/s12984-020-00681-7 10.1371/journal.pone.0186132 10.1109/TNSRE.2015.2417775 10.1088/1741-2552/aa620b 10.1016/j.cobme.2023.100462 10.1109/TNSRE.2019.2907200 10.3390/s19092203 10.1016/j.ifacol.2018.11.544 10.1016/j.neunet.2021.01.009 10.1162/NECO_a_00460 10.1152/jn.00670.2012 10.1016/j.ifacol.2023.01.109 10.1109/TBME.2022.3150665 10.1007/978-3-319-44781-0_6 10.1109/TNSRE.2012.2185066 10.1109/TNSRE.2013.2287383 10.1109/TNSRE.2020.2979743 10.1109/TBME.2023.3323601 10.1109/TBME.2023.3237081 10.1038/s41598-017-04255-x 10.1109/TNSRE.2022.3173708 10.1109/TNSRE.2023.3237181 |
| ContentType | Journal Article |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 DOA |
| DOI | 10.1109/TNSRE.2025.3545818 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 4 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 | 1037 |
| ExternalDocumentID | oai_doaj_org_article_9c117e45b6be4d6393197279edfd7a0f 40031560 10_1109_TNSRE_2025_3545818 10904858 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: Jiangsu Province Key Research and Development Program Projects grantid: SBE2023020386 funderid: 10.13039/501100013058 – fundername: European Research Council Synergy Grant NaturalBionicS grantid: 810346 – fundername: Joint Fund Project grantid: 8091B042206 – fundername: National Natural Science Foundation of China grantid: 62173089; 62303453 funderid: 10.13039/501100001809 |
| 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 7X8 |
| ID | FETCH-LOGICAL-c434t-294089015a35c55da8ab42aade105104bb1874900a4ea08c392874afb97a84c93 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001439391200004&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 | Fri Oct 03 12:28:55 EDT 2025 Thu Jul 10 17:15:52 EDT 2025 Sat May 31 02:13:59 EDT 2025 Sat Nov 29 08:11:28 EST 2025 Tue Nov 18 21:24:08 EST 2025 Wed Aug 27 01:48:44 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0/legalcode |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c434t-294089015a35c55da8ab42aade105104bb1874900a4ea08c392874afb97a84c93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0009-0001-6820-9164 0000-0001-7632-3090 0000-0002-4587-6263 0000-0002-7883-2697 0000-0002-1982-6780 |
| OpenAccessLink | https://doaj.org/article/9c117e45b6be4d6393197279edfd7a0f |
| PMID | 40031560 |
| PQID | 3173403995 |
| PQPubID | 23479 |
| PageCount | 12 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_9c117e45b6be4d6393197279edfd7a0f ieee_primary_10904858 proquest_miscellaneous_3173403995 crossref_citationtrail_10_1109_TNSRE_2025_3545818 pubmed_primary_40031560 crossref_primary_10_1109_TNSRE_2025_3545818 |
| PublicationCentury | 2000 |
| PublicationDate | 20250000 2025-00-00 20250101 2025-01-01 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – year: 2025 text: 20250000 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | IEEE transactions on neural systems and rehabilitation engineering |
| PublicationTitleAbbrev | TNSRE |
| PublicationTitleAlternate | IEEE Trans Neural Syst Rehabil Eng |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | ref13 ref12 ref15 ref14 ref11 ref10 ref17 ref16 ref19 ref18 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 Kasun (ref34) 2013; 28 ref4 ref3 ref6 ref5 ref40 ref35 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 |
| References_xml | – ident: ref29 doi: 10.1186/s12984-018-0363-1 – ident: ref9 doi: 10.1109/TRO.2015.2395731 – ident: ref13 doi: 10.1109/TBME.2019.2952890 – ident: ref26 doi: 10.1038/s41551-020-0542-9 – ident: ref40 doi: 10.1016/j.jbiomech.2010.01.027 – ident: ref1 doi: 10.1109/TBME.2022.3194104 – ident: ref42 doi: 10.1109/TNSRE.2013.2278411 – ident: ref44 doi: 10.1109/TNSRE.2020.3038374 – ident: ref14 doi: 10.1109/TNSRE.2022.3171394 – ident: ref35 doi: 10.1007/s00521-021-06233-x – ident: ref17 doi: 10.1109/TNSRE.2023.3347540 – ident: ref4 doi: 10.1109/TNSRE.2022.3181284 – ident: ref12 doi: 10.1109/LRA.2023.3330053 – volume: 28 start-page: 31 issue: 6 year: 2013 ident: ref34 article-title: Representational learning with elms for big data publication-title: IEEE Intell. Syst. – ident: ref46 doi: 10.1038/s41593-019-0555-4 – ident: ref22 doi: 10.1109/TNSRE.2015.2401134 – ident: ref45 doi: 10.1038/nn.3265 – ident: ref30 doi: 10.1073/pnas.0500199102 – ident: ref15 doi: 10.1109/TNSRE.2019.2962189 – ident: ref16 doi: 10.1109/TNSRE.2020.3029099 – ident: ref41 doi: 10.1126/scirobotics.adp3260 – ident: ref10 doi: 10.1016/j.neuron.2014.04.048 – ident: ref38 doi: 10.1109/JSEN.2021.3098120 – ident: ref2 doi: 10.1126/scirobotics.aat3630 – ident: ref23 doi: 10.1109/TNSRE.2019.2894464 – ident: ref28 doi: 10.1016/j.neunet.2013.02.008 – ident: ref37 doi: 10.1016/S1364-6613(99)01294-2 – ident: ref11 doi: 10.1186/s12984-020-00681-7 – ident: ref39 doi: 10.1371/journal.pone.0186132 – ident: ref7 doi: 10.1109/TNSRE.2015.2417775 – ident: ref49 doi: 10.1088/1741-2552/aa620b – ident: ref8 doi: 10.1016/j.cobme.2023.100462 – ident: ref19 doi: 10.1109/TNSRE.2019.2907200 – ident: ref24 doi: 10.3390/s19092203 – ident: ref43 doi: 10.1016/j.ifacol.2018.11.544 – ident: ref21 doi: 10.1016/j.neunet.2021.01.009 – ident: ref48 doi: 10.1162/NECO_a_00460 – ident: ref36 doi: 10.1152/jn.00670.2012 – ident: ref25 doi: 10.1016/j.ifacol.2023.01.109 – ident: ref20 doi: 10.1109/TBME.2022.3150665 – ident: ref31 doi: 10.1007/978-3-319-44781-0_6 – ident: ref27 doi: 10.1109/TNSRE.2012.2185066 – ident: ref47 doi: 10.1109/TNSRE.2013.2287383 – ident: ref3 doi: 10.1109/TNSRE.2020.2979743 – ident: ref33 doi: 10.1109/TBME.2023.3323601 – ident: ref32 doi: 10.1109/TBME.2023.3237081 – ident: ref6 doi: 10.1038/s41598-017-04255-x – ident: ref5 doi: 10.1109/TNSRE.2022.3173708 – ident: ref18 doi: 10.1109/TNSRE.2023.3237181 |
| SSID | ssj0017657 |
| Score | 2.4520166 |
| Snippet | Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis... |
| SourceID | doaj proquest pubmed crossref ieee |
| SourceType | Open Website Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1026 |
| SubjectTerms | Adult Algorithms Artificial Limbs Autoencoders Calibration decoder adaptation Decoding electrode shift Electrodes Electromyography Electromyography - methods Female Humans Male Manifolds Muscle, Skeletal - physiology Myoelectric control online manifold learning Online Systems Perturbation methods Reproducibility of Results Robot sensing systems Robustness Training unsupervised autoencoder Unsupervised Machine Learning User-Computer Interface |
| SummonAdditionalLinks | – databaseName: IEEE Electronic Library (IEL) dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB7RigMXyqNAeMlIwAWltePJOj6WPsShrFDZRb1FtuNIlVCy3QcS_x6Pnd1uD0XqLQ87TjIz9tjj7xuAj86iUMqXucYCc2xGJjeiVTliK61tWssjturXuRqPq8tL_WMAq0csjPc-bj7zB3QYY_lN71a0VHZImwixKqsd2FFqlMBam5CBGkVaz2DBoU1Z8DVChuvDyfjnxWmYCxblgaRAEWX42BqFIln_kF3lbkczDjhne_d81SfwePAs2VFShafwwHfP4NM2izCbJAoB9pld3CLofg7XiXKUTbvFakbdx8I37Kgxs1SA9S07D15ptww1ZzeIpY4Fn5d9_9unbDpXjh2nre_sJMIf2TRoeH7iCTg_D_fym0fuw_TsdHL8LR-yMeQOJS7zQiOvyHswsnRl2ZjKWCyMabwgw0ZrKb2f5tygN7xywfEK56a1WpkKnZYvYLfrO_8KWBHmlC4oQavaEp1wFXrNHVphtA0TTpGBWEundsOfoIwZv-s4ZeG6jhKtSaL1INEMvmzqzBJRx39LfyWhb0oSyXa8EIRYDzZbayeE8ljakfVBi6WWlKNNad-0jTK8zWCfBL_VXJJ5Bh_WOlQHY6UIjOl8v1rUwVmTyAlNnMHLpFyb2kj9a_A_X9_x1DfwiL4gLf-8hd3lfOXfwUP3Z3m1mL-PtvAPzyEGaw priority: 102 providerName: IEEE |
| Title | Online Unsupervised Adaptation of Latent Representation for Myoelectric Control During User-Decoder Co-Adaptation |
| URI | https://ieeexplore.ieee.org/document/10904858 https://www.ncbi.nlm.nih.gov/pubmed/40031560 https://www.proquest.com/docview/3173403995 https://doaj.org/article/9c117e45b6be4d6393197279edfd7a0f |
| Volume | 33 |
| WOSCitedRecordID | wos001439391200004&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 Xplore 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/eLvHCXMwrV1Lj9MwELZgxYEL4rFAWaiMBFyQWSeZ1PFx2Yc4LBVaWtSbZTu2tBJKSx8r8e-ZsdNuOQAXjklsx_HM2N_Enm8Ye-MdFEqFWmgoQUA7ssIWUQmAWDnXRidTbNW3SzUeN7OZ_rKX6ovOhGV64Dxwx9oXhQpQu5EL2FSlK0qUpXRoY6usjDT7SqW3zlS_f6BGieMTzRk7UJVyGy4j9fFk_PXqHB3Dsv5Q0a4RpfvYW5ISc3-fauXPqDOtPhcP2YMeNvKT3N1H7E7oHrO3-xTBfJL5Afg7fvUb-_YT9iPzifJpt9osaG5YhZaftHaRC_B55JcIObs11lzchiN1HAEt__xznlPlXHt-ms-187MU28inqL7iLFBU_BKfidsmD9n04nxy-kn0qRaEhwrWotQgG4IGtqp9Xbe2sQ5Ka9tQkNWCc5S7T0tpIVjZeERVeG2j08o24HX1lB108y48Z7xEh9GjhKOKNfjCNxC09OAKqx16k8WAFdvRNr4fCUqH8d0kf0RqkyRkSEKml9CAvd_VWWQWjr-W_khC3JUkBu10A_XK9Hpl_qVXA3ZIKrD3Oo2TXY2Nv97qhEFLpO0V24X5ZmUQiVUgKVR4wJ5lZdnVBpo8EVy--B9dO2L36XPzj6CX7GC93IRX7J6_WV-vlkN2V82aYTKHYQpn_AU6bAre |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB7RglQuPNsSnkYCLiitk0zW8bH0oSK2K1R2UW-W7ThSJZQs-0Di3-Oxs9vlUCRuedhxkpmxxx5_3wC8swYzIVyZSswxxXqgU501IkVsCmPqxvCArfo-FKNRdXUlv_Zg9YCFcc6FzWfugA5DLL_u7JKWyg5pEyFWZbUFd0vEnEe41jpoIAaB2NPbsG-1yPkKI8Pl4Xj07fLUzwbz8qCgUBHl-NgYhwJdf59f5XZXMww5Zw__82UfwYPet2RHURkewx3XPoH3mzzCbBxJBNgHdvkXRfdT-BlJR9mknS-n1IHMXc2Oaj2NBVjXsKH3S9uFrzm9wSy1zHu97OJ3F_PpXFt2HDe_s5MAgGQTr-PpiSPo_MzfS28euQuTs9Px8Xna52NILRa4SHOJvCL_QRelLctaV9pgrnXtMjJtNIYS_EnONTrNK-tdL3-uGyOFrtDKYg-22651z4DlflZpvRo0oinRZrZCJ7lFk2lp_JQzSyBbSUfZ_k9QzowfKkxauFRBoookqnqJJvBxXWcaqTr-WfoTCX1dkmi2wwUvRNVbrZI2y4TD0gyM83pcyIKytAnp6qYWmjcJ7JLgN5qLMk_g7UqHlDdXisHo1nXLufLuWoGc8MQJ7EflWtdG6mG9B_r8lqe-gZ3z8cVQDT-PvryA-_Q1cTHoJWwvZkv3Cu7ZX4vr-ex1sIs_krUJsg |
| 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=Online+Unsupervised+Adaptation+of+Latent+Representation+for+Myoelectric+Control+During+User-Decoder+Co-Adaptation&rft.jtitle=IEEE+transactions+on+neural+systems+and+rehabilitation+engineering&rft.au=Deng%2C+Hanjie&rft.au=Wei%2C+Zhikai&rft.au=Hu%2C+Xuhui&rft.au=Zeng%2C+Hong&rft.date=2025&rft.pub=IEEE&rft.issn=1534-4320&rft.volume=33&rft.spage=1026&rft.epage=1037&rft_id=info:doi/10.1109%2FTNSRE.2025.3545818&rft.externalDocID=10904858 |
| 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 |