A SEMG-Force Estimation Framework Based on a Fast Orthogonal Search Method Coupled with Factorization Algorithms
A novel framework based on the fast orthogonal search (FOS) method coupled with factorization algorithms was proposed and implemented to realize high-accuracy muscle force estimation via surface electromyogram (SEMG). During static isometric elbow flexion, high-density SEMG (HD-SEMG) signals were re...
Saved in:
| Published in: | Sensors (Basel, Switzerland) Vol. 18; no. 7; p. 2238 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Switzerland
MDPI AG
11.07.2018
MDPI |
| Subjects: | |
| ISSN: | 1424-8220, 1424-8220 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | A novel framework based on the fast orthogonal search (FOS) method coupled with factorization algorithms was proposed and implemented to realize high-accuracy muscle force estimation via surface electromyogram (SEMG). During static isometric elbow flexion, high-density SEMG (HD-SEMG) signals were recorded from upper arm muscles, and the generated elbow force was measured at the wrist. HD-SEMG signals were decomposed into time-invariant activation patterns and time-varying activation curves using three typical factorization algorithms including principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF). The activation signal of the target muscle was obtained by summing the activation curves, and the FOS algorithm was used to create basis functions with activation signals and establish the force estimation model. Static isometric elbow flexion experiments at three target levels were performed on seven male subjects, and the force estimation performances were compared among three typical factorization algorithms as well as a conventional method for extracting the average signal envelope of all HD-SEMG channels (AVG-ENVLP method). The overall root mean square difference (RMSD) values between the measured forces and the estimated forces obtained by different methods were 11.79 ± 4.29% for AVG-ENVLP, 9.74 ± 3.77% for PCA, 9.59 ± 3.81% for ICA, and 9.51 ± 4.82% for NMF. The results demonstrated that, compared to the conventional AVG-ENVLP method, factorization algorithms could substantially improve the performance of force estimation. The FOS method coupled with factorization algorithms provides an effective way to estimate the combined force of multiple muscles and has potential value in the fields of sports biomechanics, gait analysis, prosthesis control strategy, and exoskeleton devices for assisted rehabilitation. |
|---|---|
| AbstractList | A novel framework based on the fast orthogonal search (FOS) method coupled with factorization algorithms was proposed and implemented to realize high-accuracy muscle force estimation via surface electromyogram (SEMG). During static isometric elbow flexion, high-density SEMG (HD-SEMG) signals were recorded from upper arm muscles, and the generated elbow force was measured at the wrist. HD-SEMG signals were decomposed into time-invariant activation patterns and time-varying activation curves using three typical factorization algorithms including principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF). The activation signal of the target muscle was obtained by summing the activation curves, and the FOS algorithm was used to create basis functions with activation signals and establish the force estimation model. Static isometric elbow flexion experiments at three target levels were performed on seven male subjects, and the force estimation performances were compared among three typical factorization algorithms as well as a conventional method for extracting the average signal envelope of all HD-SEMG channels (AVG-ENVLP method). The overall root mean square difference (RMSD) values between the measured forces and the estimated forces obtained by different methods were 11.79 ± 4.29% for AVG-ENVLP, 9.74 ± 3.77% for PCA, 9.59 ± 3.81% for ICA, and 9.51 ± 4.82% for NMF. The results demonstrated that, compared to the conventional AVG-ENVLP method, factorization algorithms could substantially improve the performance of force estimation. The FOS method coupled with factorization algorithms provides an effective way to estimate the combined force of multiple muscles and has potential value in the fields of sports biomechanics, gait analysis, prosthesis control strategy, and exoskeleton devices for assisted rehabilitation. A novel framework based on the fast orthogonal search (FOS) method coupled with factorization algorithms was proposed and implemented to realize high-accuracy muscle force estimation via surface electromyogram (SEMG). During static isometric elbow flexion, high-density SEMG (HD-SEMG) signals were recorded from upper arm muscles, and the generated elbow force was measured at the wrist. HD-SEMG signals were decomposed into time-invariant activation patterns and time-varying activation curves using three typical factorization algorithms including principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF). The activation signal of the target muscle was obtained by summing the activation curves, and the FOS algorithm was used to create basis functions with activation signals and establish the force estimation model. Static isometric elbow flexion experiments at three target levels were performed on seven male subjects, and the force estimation performances were compared among three typical factorization algorithms as well as a conventional method for extracting the average signal envelope of all HD-SEMG channels (AVG-ENVLP method). The overall root mean square difference (RMSD) values between the measured forces and the estimated forces obtained by different methods were 11.79 ± 4.29% for AVG-ENVLP, 9.74 ± 3.77% for PCA, 9.59 ± 3.81% for ICA, and 9.51 ± 4.82% for NMF. The results demonstrated that, compared to the conventional AVG-ENVLP method, factorization algorithms could substantially improve the performance of force estimation. The FOS method coupled with factorization algorithms provides an effective way to estimate the combined force of multiple muscles and has potential value in the fields of sports biomechanics, gait analysis, prosthesis control strategy, and exoskeleton devices for assisted rehabilitation.A novel framework based on the fast orthogonal search (FOS) method coupled with factorization algorithms was proposed and implemented to realize high-accuracy muscle force estimation via surface electromyogram (SEMG). During static isometric elbow flexion, high-density SEMG (HD-SEMG) signals were recorded from upper arm muscles, and the generated elbow force was measured at the wrist. HD-SEMG signals were decomposed into time-invariant activation patterns and time-varying activation curves using three typical factorization algorithms including principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF). The activation signal of the target muscle was obtained by summing the activation curves, and the FOS algorithm was used to create basis functions with activation signals and establish the force estimation model. Static isometric elbow flexion experiments at three target levels were performed on seven male subjects, and the force estimation performances were compared among three typical factorization algorithms as well as a conventional method for extracting the average signal envelope of all HD-SEMG channels (AVG-ENVLP method). The overall root mean square difference (RMSD) values between the measured forces and the estimated forces obtained by different methods were 11.79 ± 4.29% for AVG-ENVLP, 9.74 ± 3.77% for PCA, 9.59 ± 3.81% for ICA, and 9.51 ± 4.82% for NMF. The results demonstrated that, compared to the conventional AVG-ENVLP method, factorization algorithms could substantially improve the performance of force estimation. The FOS method coupled with factorization algorithms provides an effective way to estimate the combined force of multiple muscles and has potential value in the fields of sports biomechanics, gait analysis, prosthesis control strategy, and exoskeleton devices for assisted rehabilitation. |
| Author | Cao, Shuai Yuan, Yuan Chen, Xun Zhang, Xu Chen, Xiang |
| AuthorAffiliation | Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China; yuan1111@mail.ustc.edu.cn (Y.Y.); caoshuai@ustc.edu.cn (S.C.); xuzhang90@ustc.edu.cn (X.Z.); xunchen@ece.ubc.ca (X.C.) |
| AuthorAffiliation_xml | – name: Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China; yuan1111@mail.ustc.edu.cn (Y.Y.); caoshuai@ustc.edu.cn (S.C.); xuzhang90@ustc.edu.cn (X.Z.); xunchen@ece.ubc.ca (X.C.) |
| Author_xml | – sequence: 1 givenname: Xiang surname: Chen fullname: Chen, Xiang – sequence: 2 givenname: Yuan surname: Yuan fullname: Yuan, Yuan – sequence: 3 givenname: Shuai surname: Cao fullname: Cao, Shuai – sequence: 4 givenname: Xu orcidid: 0000-0002-1533-4340 surname: Zhang fullname: Zhang, Xu – sequence: 5 givenname: Xun surname: Chen fullname: Chen, Xun |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29997373$$D View this record in MEDLINE/PubMed |
| BookMark | eNplks1PHCEYh0ljUz_aQ_-BhqSXepjKxwwflybrZteaaDzYngkCs8N2ZtgCU9P-9bKuGrUn4OXhyfsDDsHeGEYHwEeMvlIq0UnCAnFCqHgDDnBN6koQgvaezffBYUprhAilVLwD-0RKySmnB2Azg9eLy7NqGaJxcJGyH3T2YYTLqAd3G-IveKqTs7CUNFzqlOFVzF1YhVH38NrpaDp46UrFwnmYNn1Bb33uCmpyiP7fzjbrV2WRuyG9B29b3Sf34WE8Aj-Xix_z79XF1dn5fHZRmZrJXGkmsUGUE2zbxjpBkZUWa6QZb7BwkkshDWEtdXXJbjitRW0aJB3XreWO0CNwvvPaoNdqE0uu-FcF7dV9IcSV0jF70ztV7pBw3bSNYLJmrKitqWUr2hvbSIR5cX3buTbTzeCscWOOun8hfbkz-k6twh_FEJOUN0Xw5UEQw-_JpawGn4zrez26MCVFEBOSbJMU9PMrdB2mWC67UBiJbXq6pT497-iplceHLcDJDjAxpBRdq4zP929RGvS9wmgbG6mnr1NOHL868Sj9n70DaRbBew |
| CitedBy_id | crossref_primary_10_1109_JSEN_2023_3314104 crossref_primary_10_1186_s12984_020_00786_z crossref_primary_10_1016_j_bspc_2021_103297 crossref_primary_10_1088_1742_6596_1345_5_052062 crossref_primary_10_1109_TMECH_2021_3074800 crossref_primary_10_3390_s19030555 crossref_primary_10_1109_ACCESS_2020_2980579 crossref_primary_10_1016_j_bspc_2024_106769 crossref_primary_10_1109_TNSRE_2020_3042788 crossref_primary_10_3389_fnins_2020_00450 crossref_primary_10_3233_THC_213545 crossref_primary_10_1088_1361_6579_ac799c crossref_primary_10_1109_TAES_2023_3329797 |
| Cites_doi | 10.1109/TBME.2009.2036444 10.1080/10255842.2015.1070578 10.1371/journal.pone.0109943 10.1016/j.jelekin.2004.06.008 10.1007/BF00204124 10.1016/j.gaitpost.2006.10.012 10.3390/s18020663 10.1152/jn.00222.2005 10.1109/ICORR.2013.6650419 10.1038/44565 10.3390/s18020467 10.1016/j.jelekin.2004.01.004 10.1016/j.jelekin.2011.12.012 10.1088/1741-2552/aa63ba 10.1016/j.clinbiomech.2008.08.003 10.1159/000147199 10.1177/036354659502300421 10.1109/TNSRE.2013.2282898 10.1109/72.761722 10.1109/TBME.2006.889202 10.1109/TCYB.2014.2386856 10.1109/TBME.2006.870246 10.1109/TBME.2006.889190 10.1109/TNSRE.2016.2515087 10.1016/0021-9290(91)90347-P 10.2214/AJR.07.2947 10.1016/j.jelekin.2011.10.012 10.1016/j.humov.2013.03.003 10.1109/10.634654 10.5040/9781718225022 10.1007/s10237-011-0290-6 10.1016/j.jbiomech.2005.08.007 10.1007/978-3-319-19387-8_232 10.1007/s00422-008-0278-1 10.1109/TBME.2011.2170687 10.1159/000147942 10.1016/j.bspc.2016.11.010 10.1186/1475-925X-12-86 10.1109/JBHI.2013.2286455 10.1088/1741-2560/13/6/066001 |
| ContentType | Journal Article |
| Copyright | 2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2018 by the authors. 2018 |
| Copyright_xml | – notice: 2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2018 by the authors. 2018 |
| DBID | AAYXX CITATION NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/s18072238 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) Health Medical collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | PubMed CrossRef MEDLINE - Academic Publicly Available Content Database |
| 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: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_33927a5f5869466798dc49f8fbd59017 PMC6069375 29997373 10_3390_s18072238 |
| Genre | Journal Article |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS ADRAZ AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IPNFZ KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RIG RNS RPM TUS UKHRP XSB ~8M 3V. ABJCF ALIPV ARAPS HCIFZ KB. M7S NPM PDBOC 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c469t-a691c03721df5de830d9d1a0a67518e97989c26f3e4807c73484c509e7afd7e23 |
| IEDL.DBID | 7X7 |
| ISICitedReferencesCount | 14 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000441334300260&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1424-8220 |
| IngestDate | Mon Nov 10 04:30:51 EST 2025 Tue Nov 04 01:57:27 EST 2025 Fri Sep 05 12:56:37 EDT 2025 Tue Oct 07 07:07:06 EDT 2025 Wed Feb 19 02:40:58 EST 2025 Tue Nov 18 22:07:14 EST 2025 Sat Nov 29 07:16:04 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Keywords | factorization algorithms fast orthogonal search high-density SEMG muscle force estimation |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c469t-a691c03721df5de830d9d1a0a67518e97989c26f3e4807c73484c509e7afd7e23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-1533-4340 |
| OpenAccessLink | https://www.proquest.com/docview/2108751837?pq-origsite=%requestingapplication% |
| PMID | 29997373 |
| PQID | 2108751837 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_33927a5f5869466798dc49f8fbd59017 pubmedcentral_primary_oai_pubmedcentral_nih_gov_6069375 proquest_miscellaneous_2068924807 proquest_journals_2108751837 pubmed_primary_29997373 crossref_citationtrail_10_3390_s18072238 crossref_primary_10_3390_s18072238 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-07-11 |
| PublicationDateYYYYMMDD | 2018-07-11 |
| PublicationDate_xml | – month: 07 year: 2018 text: 2018-07-11 day: 11 |
| PublicationDecade | 2010 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationTitleAlternate | Sensors (Basel) |
| PublicationYear | 2018 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Lee (ref_37) 1999; 401 Rau (ref_42) 2009; 24 Hayashibe (ref_13) 2013; 12 ref_14 Mengarelli (ref_3) 2017; 33 ref_12 ref_34 Gregor (ref_10) 1991; 24 ref_33 Hyvarinen (ref_36) 1999; 10 Boudaoud (ref_11) 2015; 18 ref_31 Tresch (ref_38) 2006; 95 Staudenmann (ref_23) 2005; 15 Staudenmann (ref_26) 2007; 54 ref_39 Castellini (ref_6) 2009; 100 Nakamura (ref_35) 2004; 14 Clancy (ref_16) 2006; 39 Hasselman (ref_17) 1995; 23 Christophy (ref_2) 2012; 11 Staudenmann (ref_25) 2006; 53 Huang (ref_24) 2017; 14 Daley (ref_7) 2012; 22 Johns (ref_21) 2016; 24 Huang (ref_30) 2016; 13 ref_22 Segal (ref_19) 1991; 142 Korenberg (ref_40) 1989; 60 Li (ref_8) 2014; 18 Mobasser (ref_32) 2007; 54 Na (ref_43) 2016; 46 Agostini (ref_4) 2012; 59 Gyftopoulos (ref_18) 2008; 190 Wolf (ref_20) 1997; 158 ref_29 Muceli (ref_28) 2014; 22 Mountjoy (ref_41) 2010; 57 Vieira (ref_1) 2013; 32 Clancy (ref_15) 1997; 44 ref_9 Hashemi (ref_27) 2012; 22 Patikas (ref_5) 2007; 26 |
| References_xml | – volume: 57 start-page: 790 year: 2010 ident: ref_41 article-title: Use of the fast orthogonal search method to estimate optimal joint angle for upper limb Hill-muscle models publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2009.2036444 – volume: 18 start-page: 1890 year: 2015 ident: ref_11 article-title: On the benefits of using HD-sEMG technique for estimating muscle force publication-title: Comput. Methods Biomech. Biomed. Eng. doi: 10.1080/10255842.2015.1070578 – ident: ref_29 doi: 10.1371/journal.pone.0109943 – volume: 15 start-page: 1 year: 2005 ident: ref_23 article-title: Towards optimal multi-channel EMG electrode configurations in muscle force estimation: A high density EMG study publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2004.06.008 – volume: 60 start-page: 267 year: 1989 ident: ref_40 article-title: A robust orthogonal algorithm for system identification and time-series analysis publication-title: Biol. Cybern. doi: 10.1007/BF00204124 – volume: 26 start-page: 362 year: 2007 ident: ref_5 article-title: Electromyographic patterns in children with cerebral palsy: Do they change after surgery? publication-title: Gait Posture doi: 10.1016/j.gaitpost.2006.10.012 – ident: ref_14 doi: 10.3390/s18020663 – ident: ref_34 – volume: 95 start-page: 2199 year: 2006 ident: ref_38 article-title: Matrix factorization algorithms for the identification of muscle synergies: Evaluation on simulated and experimental data sets publication-title: J. Neurophysiol. doi: 10.1152/jn.00222.2005 – ident: ref_31 doi: 10.1109/ICORR.2013.6650419 – volume: 401 start-page: 788 year: 1999 ident: ref_37 article-title: Learning the parts of objects by non-negative matrix factorization publication-title: Nature doi: 10.1038/44565 – ident: ref_9 doi: 10.3390/s18020467 – volume: 14 start-page: 423 year: 2004 ident: ref_35 article-title: The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: Part I—Measuring techniques publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2004.01.004 – volume: 22 start-page: 478 year: 2012 ident: ref_7 article-title: High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2011.12.012 – volume: 14 start-page: 046005 year: 2017 ident: ref_24 article-title: An isometric muscle force estimation framework based on a high-density surface EMG array and an NMF algorithm publication-title: J. Neural Eng. doi: 10.1088/1741-2552/aa63ba – volume: 24 start-page: 225 year: 2009 ident: ref_42 article-title: Surface electromyography and muscle force: Limits in sEMG–force relationship and new approaches for applications publication-title: Clin. Biomech. doi: 10.1016/j.clinbiomech.2008.08.003 – volume: 142 start-page: 261 year: 1991 ident: ref_19 article-title: Anatomical partitioning of three multiarticular human muscles publication-title: Cells Tissues Organs doi: 10.1159/000147199 – volume: 23 start-page: 493 year: 1995 ident: ref_17 article-title: An explanation for various rectus femoris strain injuries using previously undescribed muscle architecture publication-title: Am. J. Sports Med. doi: 10.1177/036354659502300421 – volume: 22 start-page: 623 year: 2014 ident: ref_28 article-title: Extracting signals robust to electrode number and shift for online simultaneous and proportional myoelectric control by factorization algorithms publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2013.2282898 – volume: 10 start-page: 626 year: 1999 ident: ref_36 article-title: Fast and robust fixed-point algorithms for independent component analysis publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.761722 – volume: 54 start-page: 751 year: 2007 ident: ref_26 article-title: Independent component analysis of high-density electromyography in muscle force estimation publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2006.889202 – volume: 46 start-page: 2 year: 2016 ident: ref_43 article-title: A study on estimation of joint force through isometric index finger abduction with the help of SEMG peaks for biomedical applications publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2014.2386856 – volume: 53 start-page: 712 year: 2006 ident: ref_25 article-title: Improving EMG-based muscle force estimation by using a high-density EMG grid and principal component analysis publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2006.870246 – volume: 54 start-page: 683 year: 2007 ident: ref_32 article-title: Estimation of elbow-induced wrist force with EMG signals using fast orthogonal search publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2006.889190 – volume: 24 start-page: 1041 year: 2016 ident: ref_21 article-title: Force modelling of upper limb biomechanics using ensemble fast orthogonal search on high-density electromyography publication-title: IEEE Trans. Neural Syst. Rehab. Eng. doi: 10.1109/TNSRE.2016.2515087 – volume: 24 start-page: 287 year: 1991 ident: ref_10 article-title: A comparison of the triceps surae and residual muscle moments at the ankle during cycling publication-title: J. Biomech. doi: 10.1016/0021-9290(91)90347-P – ident: ref_33 – volume: 190 start-page: W182 year: 2008 ident: ref_18 article-title: Normal anatomy and strains of the deep musculotendinous junction of the proximal rectus femoris: MRI features publication-title: Am. J. Roentgenol. doi: 10.2214/AJR.07.2947 – volume: 22 start-page: 469 year: 2012 ident: ref_27 article-title: EMG–force modeling using parallel cascade identification publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2011.10.012 – ident: ref_12 – volume: 32 start-page: 753 year: 2013 ident: ref_1 article-title: How much does the human medial gastrocnemius muscle contribute to ankle torques outside the sagittal plane? publication-title: Hum. Mov. Sci. doi: 10.1016/j.humov.2013.03.003 – volume: 44 start-page: 1024 year: 1997 ident: ref_15 article-title: Relating agonist-antagonist electromyograms to joint torque during isometric, quasi-isotonic, nonfatiguing contractions publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/10.634654 – ident: ref_39 doi: 10.5040/9781718225022 – volume: 11 start-page: 19 year: 2012 ident: ref_2 article-title: A musculoskeletal model for the lumbar spine publication-title: Biomech. Model. Mechanobiol. doi: 10.1007/s10237-011-0290-6 – volume: 39 start-page: 2690 year: 2006 ident: ref_16 article-title: Influence of advanced electromyogram (EMG) amplitude processors on EMG-to-torque estimation during constant-posture, force-varying contractions publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2005.08.007 – ident: ref_22 doi: 10.1007/978-3-319-19387-8_232 – volume: 100 start-page: 35 year: 2009 ident: ref_6 article-title: Surface EMG in advanced hand prosthetics publication-title: Biol. Cybern. doi: 10.1007/s00422-008-0278-1 – volume: 59 start-page: 219 year: 2012 ident: ref_4 article-title: An algorithm for the estimation of the signal-to-noise ratio in surface myoelectric signals generated during cyclic movements publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2011.2170687 – volume: 158 start-page: 287 year: 1997 ident: ref_20 article-title: Morphological analysis of the human tibialis anterior and medial gastrocnemius muscles publication-title: Cells Tissues Organs doi: 10.1159/000147942 – volume: 33 start-page: 1 year: 2017 ident: ref_3 article-title: Co-contraction activity of ankle muscles during walking: A gender comparison publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2016.11.010 – volume: 12 start-page: 86 year: 2013 ident: ref_13 article-title: Voluntary EMG-to-force estimation with a multi-scale physiological muscle model publication-title: Biomed. Eng. Online doi: 10.1186/1475-925X-12-86 – volume: 18 start-page: 1043 year: 2014 ident: ref_8 article-title: sEMG-based joint force control for an upper-limb power-assist exoskeleton robot publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2013.2286455 – volume: 13 start-page: 066001 year: 2016 ident: ref_30 article-title: Muscle-tendon units localization and activation level analysis based on high-density surface EMG array and NMF algorithm publication-title: J. Neural Eng. doi: 10.1088/1741-2560/13/6/066001 |
| SSID | ssj0023338 |
| Score | 2.314651 |
| Snippet | A novel framework based on the fast orthogonal search (FOS) method coupled with factorization algorithms was proposed and implemented to realize high-accuracy... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 2238 |
| SubjectTerms | Algorithms Elbow factorization algorithms fast orthogonal search high-density SEMG muscle force estimation |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NT9wwEB0hxIEeEF8toVtkKg69RDhxEtvHBW3gAlSCStwix3ZYpCVZbbL8_o6TbLRbIfXCMfZEcjzjzDzb8wbgIspprAWXPremBSiJn0uW-EGgQ2mCIi_yqC02we_vxfOz_L1W6svdCevogbuJu2TowLmKi1gkjgqdS2F0JAtR5MalTbZ55JTLFZjqoRZD5NXxCOH79LIOBOXoCMWG92lJ-j-KLP-9ILnmcdJ92OtDRTLuhngAW7Y8hC9rBIJHMB-Tx8ndjZ9WC23JBJdrl4lI0tWdK3KFbsoQbFIkVXVDHhbNtHpx8TfprhqTu7aINLmulvMZirqdWRR1m_l9jiYZz17woZm-1cfwJ508Xd_6fQ0FXyPwbXyVyEBThjjPFLGxglGDKlBUJe68xUqcT6nDpGDW5ZZrx3UTaQwiLFeF4TZkX2G7rEp7AkRJzZQMpI40jyyGu_gjVaGVcWgiak3swa_V3Ga6Jxh3dS5mGQINp4ZsUIMHPwfReceq8ZHQlVPQIOCIsNsGNI-sN4_sf-bhwWil3qxfnXWGMFe4z2fYfT5047pyhyWqtNUSZWgiEJviWDz41lnDMBJ04ZIzzjzgG3ayMdTNnvJ12nJ3I17EgDA-_Yxv-w67GL4Jt9McBCPYbhZL-wN29HvzWi_O2gXxF4LQD1M priority: 102 providerName: Directory of Open Access Journals |
| Title | A SEMG-Force Estimation Framework Based on a Fast Orthogonal Search Method Coupled with Factorization Algorithms |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/29997373 https://www.proquest.com/docview/2108751837 https://www.proquest.com/docview/2068924807 https://pubmed.ncbi.nlm.nih.gov/PMC6069375 https://doaj.org/article/33927a5f5869466798dc49f8fbd59017 |
| Volume | 18 |
| WOSCitedRecordID | wos000441334300260&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: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health Medical collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwELZglwMceMMGlsogDlyiTeIktk-oXSXAoaXiIZVT5NpOu1JJuknKkd_OTJJmt2jFhUukOBNprPHY843tbwh5Gy69SAsuXW5NC1BidylZ7Pq-DqTx82W-DNtiE3w2E4uFnPcJt7o_VrmfE9uJ2pQac-RnAE0EbhEw_n576WLVKNxd7Uto3CbHWDYbxzlfXAEuBvirYxNiAO3Pal94HJZDcbAGtVT9N8WXfx-TvLbupA_-V-OH5H4fcdJxN0QekVu2eEzuXeMhfEK2Y_o1mX5w07LSlibg9d2FRpruj27RCax2hkKToqmqG_q5atblCsN42p1YptO2FjU9L3fbDYhighdEcU-gv-pJx5sVvDTrn_VT8j1Nvp1_dPtSDK4G_Ny4Kpa-9hjARZNHxgrmGbCk8lSMPbSSSyF1EOfM4hV1jZQ5oYZYxHKVG24D9owcFWVhTwhVUjMlfalDzUMLUTPMxyqwMgpM6FkTOeTd3jiZ7nnKsVzGJgO8gnbMBjs65M0guu3IOW4SmqCFBwHk024bymqV9e6JvwRcRXkkYiTch-4YHcpc5EuDl3O5Q073Ns56J6-zKwM75PXwGdwT91xUYcsdyHixAIgLujjkeTecBk0gEpCcceYQfjDQDlQ9_FJcrFsKcICdEFdGL_6t1ktyF-I7galo3z8lR021s6_IHf2ruairUesr7VOMyPEkmc2_jNqUBDynvxNom3-azn_8ASAIJL4 |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB5VBQk48H4YCiwIJC5Wba_t9R4QSktCqzYBiSLlZta766RSiEPsgPhT_EZm_EgbVHHrgaPtsTVrf56Zb3dnBuBVmHmRToR0hTU1QYndTPLY9X0dSOPnWZ6FdbMJMRol47H8tAW_u1wY2lbZ2cTaUJtC0xz5LlKThJYIuHi3-O5S1yhaXe1aaDSwOLK_fiJlK98evsfv-zoIBv2T_QO37SrgaqSClati6WuPI_MxeWRswj2DSilPxfR4K4VMpA7inFvKttZU_SXU6FatULkRlgodoMm_gnZcENkT4zOCx5HvNdWLOJfebunjA9D9Jhs-r24NcFE8-_e2zHN-bnDrf3tDt-FmG1GzXvML3IEtO78LN87VWbwHix773B9-cAfFUlvWR6vWJGyyQbc1je2hNzcMTyk2UGXFPi6raTEhmsKaHdlsWPfaZvvFajFDUZrARlFa82hTWVlvNsGDavqtvA9fLmXID2B7XsztI2BKaq6kL3WoRWiRFaC_UYGVUWBCz5rIgTcdGFLd1mGndiCzFPkY4SZd48aBl2vRRVN85CKhPULUWoDqhdcniuUkbc0P3RIIFeVRElNDARyO0aHMkzwzlHwsHNjpMJW2RqxMzwDlwIv1ZTQ_tKak5rZYoYwXJ0jhURcHHjbwXWuCkY4UXHAHxAawN1TdvDI_ndYlzpFWY9wcPf63Ws_h2sHJ8Dg9PhwdPYHrGMsmNO3u-zuwXS1X9ilc1T-q03L5rP5PGXy9bNj_AXA_eb8 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB5VKUL0wBtqKLAgkLhYsb2213tAKG1jiEpCJEAqJ7PeXSeVQhxiB8Rf49cx6xcNqrj1wDH22Bknn2fm250HwHM_dQIZMW4zrSqCEtopp6HtutLjys3SLPWrYRNsMolOT_l0B361tTAmrbK1iZWhVrk0a-R9pCaR2SKgrJ81aRHT4_j16pttJkiZndZ2nEYNkRP98wfSt-LV6Bj_6xeeFw8_Hr21mwkDtkRaWNoi5K50KLIglQVKR9RRqKBwRGi-SnPGIy69MKPaVF5L0wnGl-hiNROZYto0PUDzv4shue_1YHc6Gk8_d3SPIvurexlRyp1-4eIt0BlHWx6wGhRwUXT7d5LmOa8X3_iff6-bcL2JtcmgfjluwY5e3oa9cx0Y78BqQD4Mx2_sOF9LTYZo7-pSThK3SWvkEP28InhIkFgUJXm_Luf5zBAYUudqk3E1hZsc5ZvVAkXN0jaKmt2QpsiVDBYz_FDOvxZ34dOlPPI96C3zpd4HIrikgrtc-pL5GvkCeiLhaR54yne0Cix42QIjkU2HdjMoZJEgUzMYSjoMWfCsE13VbUkuEjo06OoETCfx6kC-niWNYTKXeEwEWRCFZtQAPo6SPs-iLFWmLJlZcNDiK2nMW5H8AZcFT7vTaJjMbpNY6nyDMk4YIblHXSy4X0O50wRjIM4ooxawLZBvqbp9Znk2r5qfI-HGiDp48G-1nsBVRHvybjQ5eQjXMMiNzHq86x5Ar1xv9CO4Ir-XZ8X6cfPSEvhy2bj_DVx9hA4 |
| 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=A+SEMG-Force+Estimation+Framework+Based+on+a+Fast+Orthogonal+Search+Method+Coupled+with+Factorization+Algorithms&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Chen%2C+Xiang&rft.au=Yuan%2C+Yuan&rft.au=Cao%2C+Shuai&rft.au=Zhang%2C+Xu&rft.date=2018-07-11&rft.pub=MDPI+AG&rft.eissn=1424-8220&rft.volume=18&rft.issue=7&rft.spage=2238&rft_id=info:doi/10.3390%2Fs18072238&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |