Consistent control information driven musculoskeletal model for multiday myoelectric control
Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degraded owing to the inherent non-stationary characteristics of EMG signals. Here, to improve the es...
Uloženo v:
| Vydáno v: | Journal of neural engineering Ročník 20; číslo 5 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
01.10.2023
|
| Témata: | |
| ISSN: | 1741-2552, 1741-2552 |
| On-line přístup: | Zjistit podrobnosti o přístupu |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degraded owing to the inherent non-stationary characteristics of EMG signals. Here, to improve the estimation performance without retraining, we proposed a consistent muscle excitation extraction approach based on an improved non-negative matrix factorization (NMF) algorithm for MM when applied to simultaneous hand and wrist movement prediction.
We added constraints and
-norm regularization terms to the objective function of classic NMF regarding muscle weighting matrix and time-varying profiles, through which stable muscle synergies across days were identified. The resultant profiles of these synergies were then used to drive the MM. Both offline and online experiments were conducted to evaluate the performance of the proposed method in inter-day scenarios.
The results demonstrated significantly better and more robust performance over several competitive methods in inter-day experiments, including machine learning methods, EMG envelope-driven MM, and classic NMF-based MM. Furthermore, the analysis of control information on different days revealed the effectiveness of the proposed method in obtaining consistent muscle excitations.
The outcomes potentially provide a novel and promising pathway for the robust and zero-retraining control of myoelectric interfaces. |
|---|---|
| AbstractList | Objective.Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degraded owing to the inherent non-stationary characteristics of EMG signals. Here, to improve the estimation performance without retraining, we proposed a consistent muscle excitation extraction approach based on an improved non-negative matrix factorization (NMF) algorithm for MM when applied to simultaneous hand and wrist movement prediction.Approach.We added constraints andL2-norm regularization terms to the objective function of classic NMF regarding muscle weighting matrix and time-varying profiles, through which stable muscle synergies across days were identified. The resultant profiles of these synergies were then used to drive the MM. Both offline and online experiments were conducted to evaluate the performance of the proposed method in inter-day scenarios.Main results.The results demonstrated significantly better and more robust performance over several competitive methods in inter-day experiments, including machine learning methods, EMG envelope-driven MM, and classic NMF-based MM. Furthermore, the analysis of control information on different days revealed the effectiveness of the proposed method in obtaining consistent muscle excitations.Significance.The outcomes potentially provide a novel and promising pathway for the robust and zero-retraining control of myoelectric interfaces.Objective.Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degraded owing to the inherent non-stationary characteristics of EMG signals. Here, to improve the estimation performance without retraining, we proposed a consistent muscle excitation extraction approach based on an improved non-negative matrix factorization (NMF) algorithm for MM when applied to simultaneous hand and wrist movement prediction.Approach.We added constraints andL2-norm regularization terms to the objective function of classic NMF regarding muscle weighting matrix and time-varying profiles, through which stable muscle synergies across days were identified. The resultant profiles of these synergies were then used to drive the MM. Both offline and online experiments were conducted to evaluate the performance of the proposed method in inter-day scenarios.Main results.The results demonstrated significantly better and more robust performance over several competitive methods in inter-day experiments, including machine learning methods, EMG envelope-driven MM, and classic NMF-based MM. Furthermore, the analysis of control information on different days revealed the effectiveness of the proposed method in obtaining consistent muscle excitations.Significance.The outcomes potentially provide a novel and promising pathway for the robust and zero-retraining control of myoelectric interfaces. Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degraded owing to the inherent non-stationary characteristics of EMG signals. Here, to improve the estimation performance without retraining, we proposed a consistent muscle excitation extraction approach based on an improved non-negative matrix factorization (NMF) algorithm for MM when applied to simultaneous hand and wrist movement prediction. We added constraints and -norm regularization terms to the objective function of classic NMF regarding muscle weighting matrix and time-varying profiles, through which stable muscle synergies across days were identified. The resultant profiles of these synergies were then used to drive the MM. Both offline and online experiments were conducted to evaluate the performance of the proposed method in inter-day scenarios. The results demonstrated significantly better and more robust performance over several competitive methods in inter-day experiments, including machine learning methods, EMG envelope-driven MM, and classic NMF-based MM. Furthermore, the analysis of control information on different days revealed the effectiveness of the proposed method in obtaining consistent muscle excitations. The outcomes potentially provide a novel and promising pathway for the robust and zero-retraining control of myoelectric interfaces. |
| Author | Zhu, Xiangyang Yu, Yang Zhao, Jiamin Sheng, Xinjun |
| Author_xml | – sequence: 1 givenname: Jiamin orcidid: 0000-0002-3573-486X surname: Zhao fullname: Zhao, Jiamin organization: State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China – sequence: 2 givenname: Yang orcidid: 0000-0001-5582-4558 surname: Yu fullname: Yu, Yang organization: State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China – sequence: 3 givenname: Xinjun surname: Sheng fullname: Sheng, Xinjun organization: Meta Robotics Institute, Shanghai Jiao Tong University, Shanghai, People's Republic of China – sequence: 4 givenname: Xiangyang surname: Zhu fullname: Zhu, Xiangyang organization: Meta Robotics Institute, Shanghai Jiao Tong University, Shanghai, People's Republic of China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37567218$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkEtLAzEUhYMo9qF7V5Klm7F5TGYySym-oOCmS2FIMzcQzaMmGaH_3gFbcXUu53wcOHeBzkMMgNANJfeUSLmibU0rJgRbKQ2m42do_med_7tnaJHzByGcth25RDPeiqZlVM7R-zqGbHOBULCOoaTosA0mJq-KjQEPyX5DwH7MenQxf4KDohz2cQCHJ2xKXLGDOmB_iFOoS7L61HSFLoxyGa6PukTbp8ft-qXavD2_rh82la4pKZWRwKgRvKlbYbigTHfSANGG7qCWTFAqpiGGM9FqrmjdKMM6stOybiiDgS3R3W_tPsWvEXLpvc0anFMB4ph7JsW0nDDWTejtER13HoZ-n6xX6dCfHsJ-AF3DZlM |
| CitedBy_id | crossref_primary_10_1109_TNSRE_2025_3596110 crossref_primary_10_1109_TNSRE_2024_3509859 crossref_primary_10_1109_TNSRE_2025_3599062 crossref_primary_10_1002_aisy_202400569 |
| ContentType | Journal Article |
| Copyright | 2023 IOP Publishing Ltd. |
| Copyright_xml | – notice: 2023 IOP Publishing Ltd. |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1088/1741-2552/acef93 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| 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: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Anatomy & Physiology |
| EISSN | 1741-2552 |
| ExternalDocumentID | 37567218 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | --- 1JI 4.4 53G 5B3 5GY 5VS 5ZH 7.M 7.Q AAGCD AAJIO AAJKP AATNI ABHWH ABJNI ABQJV ABVAM ACAFW ACGFS ACHIP ADEQX AEFHF AEINN AENEX AFYNE AKPSB ALMA_UNASSIGNED_HOLDINGS AOAED ASPBG ATQHT AVWKF AZFZN CEBXE CGR CJUJL CRLBU CS3 CUY CVF DU5 EBS ECM EDWGO EIF EMSAF EPQRW EQZZN F5P IHE IJHAN IOP IZVLO KOT LAP N5L N9A NPM P2P PJBAE RIN RO9 ROL RPA SY9 W28 XPP 7X8 |
| ID | FETCH-LOGICAL-c410t-f8e21f536475f3512c98fe0cf1be4825115741f3257c3a146af290bc84612ed2 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 6 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001065910800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1741-2552 |
| IngestDate | Fri Sep 05 06:45:44 EDT 2025 Sun Oct 12 01:40:41 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Keywords | non-negative matrix factorization algorithm musculoskeletal model myoelectric interface multiday zero-retraining |
| Language | English |
| License | 2023 IOP Publishing Ltd. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c410t-f8e21f536475f3512c98fe0cf1be4825115741f3257c3a146af290bc84612ed2 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-3573-486X 0000-0001-5582-4558 |
| OpenAccessLink | https://doi.org/10.1088/1741-2552/acef93 |
| PMID | 37567218 |
| PQID | 2850310229 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2850310229 pubmed_primary_37567218 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-10-01 |
| PublicationDateYYYYMMDD | 2023-10-01 |
| PublicationDate_xml | – month: 10 year: 2023 text: 2023-10-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Journal of neural engineering |
| PublicationTitleAlternate | J Neural Eng |
| PublicationYear | 2023 |
| SSID | ssj0031790 |
| Score | 2.4088876 |
| Snippet | Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography... Objective.Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| SubjectTerms | Algorithms Electromyography - methods Hand - physiology Humans Muscle, Skeletal - physiology Upper Extremity Wrist - physiology |
| Title | Consistent control information driven musculoskeletal model for multiday myoelectric control |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/37567218 https://www.proquest.com/docview/2850310229 |
| Volume | 20 |
| WOSCitedRecordID | wos001065910800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV05a8MwFBZt06FLr_RIL1Qo3Uwk-ZA9lVAaujRkyJChEGwdEErsNE4K-fd9T1bSqVDo4sHYxugd-qT36X2EPCQRU4xrLP0zGUSJsUFRaEiG0jKlJM-MLpzYhBwM0vE4G_oNt9rTKjc50SVqXSncI--KNMYulkJkT_PPAFWjsLrqJTR2SSsEKIOULjneVhFC7D7VHIjkAUBn4cuUEFjd7b1urozFsvNvANNNNP2j__7iMTn0EJP2Gp84ITumPCXtXgnL69maPlJH-nS76W3y7gQ7wdLlknrWOvW9VNFiVC8wG9LZCvmqVf0BkxSgder0cyg8Rh0hUedrOltXjaTOVG2-dEZG_ZfR82vgBRcCFXG2DGxqBLcxtpSPbQhQQGWpNUxZXpgIz7jyGIbNhhDmKswhx-ZWZKxQgGG4MFqck72yKs0loVEkDSwclWXGRjLVGOzgFHGiAeFlxnbI_WYIJ-DPWKTIS1Ot6snPIHbIRWOHybxpvDEJZZzAijW9-sPb1-QAleEb3t0NaVmIZnNL9tXXclov7pyjwHUwfPsGQQHJrw |
| linkProvider | ProQuest |
| 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=Consistent+control+information+driven+musculoskeletal+model+for+multiday+myoelectric+control&rft.jtitle=Journal+of+neural+engineering&rft.au=Zhao%2C+Jiamin&rft.au=Yu%2C+Yang&rft.au=Sheng%2C+Xinjun&rft.au=Zhu%2C+Xiangyang&rft.date=2023-10-01&rft.eissn=1741-2552&rft.volume=20&rft.issue=5&rft_id=info:doi/10.1088%2F1741-2552%2Facef93&rft_id=info%3Apmid%2F37567218&rft_id=info%3Apmid%2F37567218&rft.externalDocID=37567218 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1741-2552&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1741-2552&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1741-2552&client=summon |