Applying Machine Learning to Finger Movements Using Electromyography and Visualization in Opensim
Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human–computer interface that controls interaction through hand gestures. Although there is a gap between academic public...
Uložené v:
| Vydané v: | Sensors (Basel, Switzerland) Ročník 22; číslo 10; s. 3737 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Switzerland
MDPI AG
01.05.2022
MDPI |
| Predmet: | |
| ISSN: | 1424-8220, 1424-8220 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human–computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods—the forward sequential selection method and the feature normalization method—were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results—the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis. |
|---|---|
| AbstractList | Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human-computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods-the forward sequential selection method and the feature normalization method-were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results-the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis. Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human-computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods-the forward sequential selection method and the feature normalization method-were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results-the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis.Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human-computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods-the forward sequential selection method and the feature normalization method-were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results-the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis. |
| Audience | Academic |
| Author | Gonzalez-Navarro, Felix F. Lopez-Avitia, Roberto Bravo-Zanoguera, Miguel Reyna, M. A. Amezquita-Garcia, Jose |
| AuthorAffiliation | 3 Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico; fernando.gonzalez@uabc.edu.mx (F.F.G.-N.); mreyna@uabc.edu.mx (M.A.R.) 1 Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico; jose.amezquita@uabc.edu.mx (J.A.-G.); ravitia@uabc.edu.mx (R.L.-A.) 2 Ingeniería en Mecatrónica, Universidad Politécnica de Baja California, Mexicali 21376, Mexico |
| AuthorAffiliation_xml | – name: 2 Ingeniería en Mecatrónica, Universidad Politécnica de Baja California, Mexicali 21376, Mexico – name: 3 Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico; fernando.gonzalez@uabc.edu.mx (F.F.G.-N.); mreyna@uabc.edu.mx (M.A.R.) – name: 1 Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico; jose.amezquita@uabc.edu.mx (J.A.-G.); ravitia@uabc.edu.mx (R.L.-A.) |
| Author_xml | – sequence: 1 givenname: Jose orcidid: 0000-0002-8472-115X surname: Amezquita-Garcia fullname: Amezquita-Garcia, Jose – sequence: 2 givenname: Miguel orcidid: 0000-0003-1227-9726 surname: Bravo-Zanoguera fullname: Bravo-Zanoguera, Miguel – sequence: 3 givenname: Felix F. orcidid: 0000-0002-9627-676X surname: Gonzalez-Navarro fullname: Gonzalez-Navarro, Felix F. – sequence: 4 givenname: Roberto orcidid: 0000-0003-3615-6560 surname: Lopez-Avitia fullname: Lopez-Avitia, Roberto – sequence: 5 givenname: M. A. surname: Reyna fullname: Reyna, M. A. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35632146$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkk1v3CAQhq0qVfPRHvoHKku9pIdN-DLgS6VVlLSRNsql6RWNMeyyssEFb6Ttry_OJmk2rTgMmnnngRfmuDjwwZui-IjRGaU1Ok-EYEQFFW-KI8wIm0lC0MGL_WFxnNIaIUIple-KQ1pxSjDjRwXMh6HbOr8sb0CvnDflwkD0U2IM5VWOJpY34d70xo-pvEtT5bIzeoyh34ZlhGG1LcG35U-XNtC53zC64Evny9vB-OT698VbC10yHx7jSXF3dfnj4vtscfvt-mK-mOmK8nHWaiTqpuUA1iCNK-CM07q1DQjJWyJ0U1PgHFspiZXYCOC1tpKiCoE2BNGT4nrHbQOs1RBdD3GrAjj1kAhxqSCOTndGIS2spczyimOWoY0WjdWV4BQaa4TMrK871rBpetPq7D1Ctwfdr3i3Ustwr2rMGOM4A04fATH82pg0qt4lbboOvAmbpAgXmAhC2XTvz6-k67CJPj_VpEKMUCSqv6olZAPO25DP1RNUzYXEHCMpaVad_UeVV2t6p_PQWJfzew2fXhp9dvg0IFlwvhPoGFKKxirtxocvzmTXKYzUNILqeQRzx5dXHU_Qf7V_ABM92mE |
| CitedBy_id | crossref_primary_10_3389_fnbot_2022_856797 crossref_primary_10_1109_JSEN_2023_3344700 crossref_primary_10_3390_s22207966 crossref_primary_10_1080_10447318_2025_2531277 |
| Cites_doi | 10.3103/S0735272719010047 10.1016/j.bspc.2020.101872 10.1109/ACCESS.2021.3129454 10.1155/2021/9194578 10.1109/EMBC.2015.7319780 10.1016/j.eswa.2020.113281 10.1016/j.eswa.2012.01.102 10.1002/mus.24631 10.1186/1743-0003-7-21 10.1016/j.jelekin.2019.07.008 10.1109/TBME.2007.912673 10.1016/j.medengphy.2018.04.007 10.1016/j.sna.2011.09.039 10.3389/fnbot.2019.00043 10.1016/j.patrec.2008.05.021 10.1016/j.procs.2018.07.012 10.1186/1743-0003-7-53 10.3390/ecsa-8-11262 10.1109/ISSBES.2015.7435877 10.1016/S1672-6529(16)60377-3 10.1016/0021-9290(93)90086-T 10.1016/j.neucom.2020.03.009 10.1109/TBME.1987.326033 10.1109/IEMBS.2007.4353640 10.1007/978-1-4615-5689-3 10.1109/ICARCV.2012.6485374 10.1016/j.neunet.2014.03.010 10.1002/9781119082934 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2022 MDPI AG 2022 by the authors. 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: COPYRIGHT 2022 MDPI AG – notice: 2022 by the authors. 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF 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/s22103737 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) ProQuest 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 Open Access Full Text |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) 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 | MEDLINE 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: ProQuest Publicly Available Content url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_0c7ff34f65614a66bc7bfc5763abfe78 PMC9144461 A781610883 35632146 10_3390_s22103737 |
| Genre | Journal Article |
| GeographicLocations | Mexico |
| GeographicLocations_xml | – name: Mexico |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS 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 IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M ALIPV CGR CUY CVF ECM EIF NPM 3V. 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c536t-dc079bd6aafe0c15a64639dfba786d27cb93a661f882f81e7a69cf83050ace203 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 6 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000801742700001&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 | Fri Oct 03 12:39:33 EDT 2025 Tue Nov 04 01:48:12 EST 2025 Sun Nov 09 12:50:10 EST 2025 Tue Oct 07 07:11:57 EDT 2025 Tue Nov 11 11:12:10 EST 2025 Tue Nov 04 18:29:24 EST 2025 Mon Jul 21 06:03:23 EDT 2025 Sat Nov 29 07:12:36 EST 2025 Tue Nov 18 19:58:33 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Keywords | electromyography biomechanical simulation classification model |
| 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 (https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c536t-dc079bd6aafe0c15a64639dfba786d27cb93a661f882f81e7a69cf83050ace203 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-8472-115X 0000-0003-3615-6560 0000-0002-9627-676X 0000-0003-1227-9726 |
| OpenAccessLink | https://doaj.org/article/0c7ff34f65614a66bc7bfc5763abfe78 |
| PMID | 35632146 |
| PQID | 2670423075 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_0c7ff34f65614a66bc7bfc5763abfe78 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9144461 proquest_miscellaneous_2671272340 proquest_journals_2670423075 gale_infotracmisc_A781610883 gale_infotracacademiconefile_A781610883 pubmed_primary_35632146 crossref_citationtrail_10_3390_s22103737 crossref_primary_10_3390_s22103737 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-05-01 |
| PublicationDateYYYYMMDD | 2022-05-01 |
| PublicationDate_xml | – month: 05 year: 2022 text: 2022-05-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationTitleAlternate | Sensors (Basel) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Neyroud (ref_7) 2014; 115 Wang (ref_15) 2017; 14 ref_35 ref_34 Khalaj (ref_21) 2021; 9 Koh (ref_8) 1993; 26 ref_32 (ref_16) 2014; 32 ref_30 Nishihara (ref_3) 2008; 55 Saikia (ref_10) 2018; 133 Shafiei (ref_22) 2021; 2021 ref_39 Rabin (ref_19) 2020; 149 Lei (ref_36) 2001; 290 Atkinson (ref_38) 2011; 172 Tuncer (ref_20) 2020; 58 Khushaba (ref_33) 2014; 55 Besomi (ref_31) 2019; 48 Dennis (ref_12) 2010; 7 Vonsevych (ref_13) 2019; 62 Hary (ref_9) 1987; BME-34 ref_25 Arjunan (ref_11) 2010; 7 ref_24 ref_23 Lisboa (ref_40) 2008; 29 ref_43 ref_42 ref_41 Phinyomark (ref_37) 2012; 6 ref_1 Green (ref_6) 2015; 52 ref_2 ref_29 ref_28 ref_27 ref_26 Christov (ref_5) 2018; 57 ref_4 Shahzad (ref_14) 2019; 13 (ref_17) 2020; Volume 75 Jia (ref_18) 2020; 401 |
| References_xml | – ident: ref_28 – ident: ref_30 – volume: 62 start-page: 23 year: 2019 ident: ref_13 article-title: Fingers Movements Control System Based on Artificial Neural Network Model publication-title: Radioelectron. Commun. Syst. doi: 10.3103/S0735272719010047 – volume: 58 start-page: 101872 year: 2020 ident: ref_20 article-title: Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2020.101872 – volume: 9 start-page: 156930 year: 2021 ident: ref_21 article-title: Hybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3129454 – ident: ref_24 – volume: 2021 start-page: 1 year: 2021 ident: ref_22 article-title: A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing publication-title: Math. Probl. Eng. doi: 10.1155/2021/9194578 – ident: ref_26 – ident: ref_34 – ident: ref_2 doi: 10.1109/EMBC.2015.7319780 – volume: 149 start-page: 113281 year: 2020 ident: ref_19 article-title: Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.113281 – volume: 6 start-page: 7420 year: 2012 ident: ref_37 article-title: Feature reduction and selection for EMG signal classification publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2012.01.102 – ident: ref_35 – ident: ref_23 – volume: 52 start-page: 818 year: 2015 ident: ref_6 article-title: Flexor carpi radialis surface electromyography electrode placement for evoked and voluntary measures publication-title: Muscle Nerve doi: 10.1002/mus.24631 – volume: 7 start-page: 21 year: 2010 ident: ref_12 article-title: Study of stability of time-domain features for electromyographic pattern recognition publication-title: J. NeuroEng. Rehabil. doi: 10.1186/1743-0003-7-21 – volume: 48 start-page: 128 year: 2019 ident: ref_31 article-title: Consensus for experimental design in electromyography (CEDE) project: Electrode selection matrix publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2019.07.008 – volume: 55 start-page: 636 year: 2008 ident: ref_3 article-title: Investigation of Optimum Electrode Locations by Using an Automatized Surface Electromyography Analysis Technique publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2007.912673 – volume: 57 start-page: 110 year: 2018 ident: ref_5 article-title: Separation of electrocardiographic from electromyographic signals using dynamic ltration publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2018.04.007 – volume: 172 start-page: 570 year: 2011 ident: ref_38 article-title: A comparison study of pattern recognition algorithms implemented on a microcontroller for use in an electronic tongue for monitoring drinking waters publication-title: Sens. Actuators A Phys. doi: 10.1016/j.sna.2011.09.039 – volume: 13 start-page: 43 year: 2019 ident: ref_14 article-title: Enhanced Performance for Multi-Forearm Movement Decoding Using Hybrid IMU–sEMG Interface publication-title: Front. Neurorobotics doi: 10.3389/fnbot.2019.00043 – ident: ref_25 – volume: 29 start-page: 1814 year: 2008 ident: ref_40 article-title: Cluster-based visualisation with scatter matrices publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2008.05.021 – ident: ref_29 – ident: ref_27 – volume: 133 start-page: 92 year: 2018 ident: ref_10 article-title: Combination of EMG Features and Stability Index for Finger Movements Recognition publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2018.07.012 – volume: 7 start-page: 110 year: 2010 ident: ref_11 article-title: Decoding subtle forearm exions using fractal features of surface electromyogram from single and multiple sensors publication-title: J. NeuroEng. Rehabil. doi: 10.1186/1743-0003-7-53 – ident: ref_42 doi: 10.3390/ecsa-8-11262 – volume: 290 start-page: 297 year: 2001 ident: ref_36 article-title: Detecting nonlinearity of action surface EMG signal publication-title: Phys. Lett. Sect. A Gener. Atom. Solid State Phys. – ident: ref_41 – ident: ref_1 doi: 10.1109/ISSBES.2015.7435877 – volume: 14 start-page: 47 year: 2017 ident: ref_15 article-title: Design and Myoelectric Control of an Anthropomorphic Prosthetic Hand publication-title: J. Bionic Eng. doi: 10.1016/S1672-6529(16)60377-3 – volume: 32 start-page: 279 year: 2014 ident: ref_16 article-title: Anthropomorphic Robotic Hands: A Review publication-title: Ing. Desarro. – volume: 115 start-page: 627 year: 2014 ident: ref_7 article-title: Influence of inter-electrode distance, contraction type, and muscle on the relationship between the sEMG power spectrum and contraction force publication-title: Eur. J. Appl. Physiol. – volume: 26 start-page: 151 year: 1993 ident: ref_8 article-title: Evaluation of methods to minimize cross talk in surface electromyography publication-title: J. Biomech. doi: 10.1016/0021-9290(93)90086-T – volume: 401 start-page: 236 year: 2020 ident: ref_18 article-title: Classification of electromyographic hand gesture signals using machine learning techniques publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.03.009 – volume: BME-34 start-page: 91 year: 1987 ident: ref_9 article-title: Circuit Models and Simulation Analysis of Electromyographic Signal Sources—I: The Impedance of EMG Electrodes publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.1987.326033 – ident: ref_4 doi: 10.1109/IEMBS.2007.4353640 – ident: ref_39 doi: 10.1007/978-1-4615-5689-3 – volume: Volume 75 start-page: 218 year: 2020 ident: ref_17 article-title: Hand Movement Detection from Surface Electromyography Signals by Machine Learning Techniques publication-title: CLAIB 2019 – ident: ref_32 doi: 10.1109/ICARCV.2012.6485374 – volume: 55 start-page: 42 year: 2014 ident: ref_33 article-title: Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features publication-title: Neural Netw. doi: 10.1016/j.neunet.2014.03.010 – ident: ref_43 doi: 10.1002/9781119082934 |
| SSID | ssj0023338 |
| Score | 2.412777 |
| Snippet | Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 3737 |
| SubjectTerms | Analysis Artificial Limbs Bayes Theorem biomechanical simulation Classification classification model Electrodes Electromyography Electromyography - methods Humans Implants, Artificial Machine Learning Pattern Recognition, Automated - methods Prostheses Prosthesis Sensors Signal Processing, Computer-Assisted Technology application |
| SummonAdditionalLinks | – databaseName: ProQuest Health & Medical Collection dbid: 7X7 link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagcIADb2igIIOQ4BLVsRM7PqGCuuJAKw5Q7S3ys41Ek3aTIvHv8Tje7UYgLlzXE8mjeXtnvkHobVUZyZWQuTS-zkttda4ccXkVYp8L4YnaOOF98kUcH9fLpfyaHtyG1Fa59onRUdvewBv5PuUCWjhChPtwcZnD1ij4dzWt0LiJbsHabNBzsbwuuFiovyY0IRZK-_2BUpiKg5XnWzEoQvX_6ZC3ItK8W3Ir_Czu_-_FH6B7KfHEB5OmPEQ3XPcI3d2CI3yMFKSkMPaEj2KLpcMJffUUjz1exAdAfNRHhPFxwLHbAB9Oe3TOfyXsa6w6i0_aAYY1pxFP3HYY-laG9vwJ-r44_Pbpc55WMOSmYnzMrSFCasuV8o6YolK8DCmN9VqJmlsqjJZMhRDvQ6Lu68IJxUHowYkQZRwl7Cna6frO7SIcSAtpNTNcmJJoUsvKc1NLzwpKrK4y9H4tlMYkfHJYk_GjCXUKyK_ZyC9DbzakFxMox9-IPoJkNwSAox1_6FenTTLLhhjhPSs9B0DUwIg2QnsTajCmtHeiztA70IsGrD1cxqg0tBBYAtys5kDUIWUOnpplaG9GGazUzI_X2tEkLzE016qRodebY_gSOt86119FmoIKykqSoWeTIm5YYhWHPVM8Q2KmojOe5yddexYxxGUopEtePP_3tV6gOxTGPWKD5x7aGVdX7iW6bX6O7bB6FY3tN5kzNaU priority: 102 providerName: ProQuest |
| Title | Applying Machine Learning to Finger Movements Using Electromyography and Visualization in Opensim |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35632146 https://www.proquest.com/docview/2670423075 https://www.proquest.com/docview/2671272340 https://pubmed.ncbi.nlm.nih.gov/PMC9144461 https://doaj.org/article/0c7ff34f65614a66bc7bfc5763abfe78 |
| Volume | 22 |
| WOSCitedRecordID | wos000801742700001&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: ProQuest Publicly Available Content 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/eLvHCXMwrV3di9QwEB_09MF7EL-tnksUQV_KtU2btI93souCuyyix_pUkjQ5C173uPYEX_zbnUm7ZYuCL77kIZlCMpnJzJSZ3wC8zjJTCCWLsDAuD1Nd6VDZyIYZ2j6L5impfIX32Ue5WuWbTbHea_VFOWE9PHDPuOPISOd46gRBViohtJHaGfSSudLOSl_mG8liF0wNoRbHyKvHEeIY1B-3SUL1cNTsfM_6eJD-P5_iPVs0zZPcMzyLe3B38BjZSb_T-3DDNg_gcA9H8CEo8iWpXoktfW6kZQNs6jnrtmzh_9yx5dZDg3ct82kCbN43wLn4OYBWM9VU7Kxuqcqyr81kdcMo4aStLx7Bl8X887v34dA7ITQZF11YGWSKroRSzkYmzpRI0RepnFYyF1UijS44cjN26GG7PLZSCbot1P5IGZtE_DEcNNvGPgWGpHFRaW6ENGmko7zInDB54XicRJXOAni742lpBmBx6m_xvcQAg9hfjuwP4NVIetmjafyN6JQuZiQgAGw_gWJRDmJR_kssAnhD11qSmuJmjBqqDfBIBHhVnsgcfV18YnkARxNKVC8zXd4JRjmod1smQlI-EbpbAbwcl-lLSllr7Pba08SJTHgaBfCkl6PxSDwT1CBKBCAnEjY583Slqb958O8CI-BUxM_-B5Oew52Eqjl8_uYRHHRX1_YF3DY_urq9msFNuZF-zGdw63S-Wn-aeS3DcflrjnPrD8v119-eJy74 |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwEB2VggQc-KYEChgEgkvUxE7s-IBQga5adXfFoVR7C45jtyvRpGy2oP4pfiMeJ7vdCMStB67xJMokM_M8ycwbgFdpqiVXQoZS2yxMirIIlYlMmDrsMw6eaOk7vA-HYjzOJhP5eQ1-LXphsKxyERN9oC5rjd_ItygXWMLhEO796fcQp0bh39XFCI3WLPbN-U-XsjXv9j659_ua0sHOwcfdsJsqEOqU8XlY6kjIouRKWRPpOFU8cShd2kKJjJdU6EIy5VDLur2nzWIjFEc9nF9EShsaMXfdK3DVxXGByZ6YXCR4zOV7LXsRYzLaaijFLjwcsb6CeX40wJ8AsIKA_erMFbgb3P7fHtQduNVtrMl26wl3Yc1U9-DmCt3ifVC45ca2LjLyJaSGdOyyR2Rek4H_wElGtWdQnzfEV1OQnXZO0Ml5x-1NVFWSw2mDzahtCyuZVgTrcprpyQP4cik6PoT1qq7MIyBONJZlwTQXOomKKJOp5TqTlsU0Kos0gLcLI8h1x7-OY0C-5S4PQ3vJl_YSwMul6GlLOvI3oQ9oSUsB5An3B-rZUd6FnTzSwlqWWI6Er06RQovCapdjMlVYI7IA3qAd5hjN3M1o1TVlOJWQFyzfFplLCRwSsQA2e5IuCun-8sIa8y4KNvmFKQbwYrmMZ2JlX2XqMy8TU0FZEgWw0Rr-UiWWcpyjxQMQPZfo6dxfqabHniNdxkmS8Pjxv2_rOVzfPRgN8-HeeP8J3KDY2uKLWTdhfT47M0_hmv4xnzazZ97RCXy9bIf5DdkplA8 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLUJw4P0IFDAIBJdoHTuxkwNChXbFqt3VHqBqT8Fx7LISTcpmC-pf49fhSbJhIxC3HrjGkyiTzMznSWa-AXgRRToRSiZ-om3sh1me-cpQ40cO-4yDJ5bXHd4H-3I6jQ8Pk9kG_Fz1wmBZ5Som1oE6LzV-Ix8yIbGEwyHc0LZlEbOd0dvTbz5OkMI_ratxGo2J7JnzHy59q96Md9y7fsnYaPfj-w9-O2HA1xEXSz_XVCZZLpSyhuogUiJ0iJ3bTMlY5EzqLOHKIZh1-1AbB0YqgTo5H6FKG0a5u-4l2HRb8pANYHM2nsyOunSPu-yv4TLiPKHDijHsycOB62sIWA8K-BMO1vCwX6u5Bn6jG__zY7sJ19stN9lufOQWbJjiNlxbI2K8Awo349jwRSZ1cakhLe_sMVmWZFR_-iSTsuZWX1akrrMgu80EoZPzlvWbqCInB_MK21Sb5lYyLwhW7FTzk7vw6UJ0vAeDoizMAyBONEjyjGshdUgzGieRFTpOLA8YzbPIg9crg0h1y8yOA0K-pi5DQ9tJO9vx4HknetrQkfxN6B1aVSeADOL1gXJxnLYBKaVaWstDK5AK1imSaZlZ7bJPrjJrZOzBK7TJFOOcuxmt2nYNpxIyhqXbMnbJgsMo7sFWT9LFJ91fXllm2sbHKv1tlh4865bxTKz5K0x5VssETDIeUg_uN07QqcQjgRO2hAey5x49nfsrxfxLzZ6eBGEYiuDhv2_rKVxxfpLuj6d7j-Aqw56Xusp1CwbLxZl5DJf19-W8WjxpvZ7A54v2mF9o2J5e |
| 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=Applying+Machine+Learning+to+Finger+Movements+Using+Electromyography+and+Visualization+in+Opensim&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Jose+Amezquita-Garcia&rft.au=Miguel+Bravo-Zanoguera&rft.au=Felix+F.+Gonzalez-Navarro&rft.au=Roberto+Lopez-Avitia&rft.date=2022-05-01&rft.pub=MDPI+AG&rft.eissn=1424-8220&rft.volume=22&rft.issue=10&rft.spage=3737&rft_id=info:doi/10.3390%2Fs22103737&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_0c7ff34f65614a66bc7bfc5763abfe78 |
| 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 |