Portable EMG Data Acquisition Module for Upper Limb Prosthesis Application
Electromyography (EMG) signals are gaining popularity to develop the prosthetics. In this paper, an efficient multi-channel EMG signal acquisition system has been proposed for upper limb prosthetic application. Various arm exercises have been performed to obtain EMG signals from five different arm m...
Saved in:
| Published in | IEEE sensors journal Vol. 18; no. 8; pp. 3436 - 3443 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
15.04.2018
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Electromyography (EMG) signals are gaining popularity to develop the prosthetics. In this paper, an efficient multi-channel EMG signal acquisition system has been proposed for upper limb prosthetic application. Various arm exercises have been performed to obtain EMG signals from five different arm muscles for the validation of developed hardware. The muscle's position has been selected by palpation method. Furthermore, the classification algorithms have been examined for seven different activities. Total 29 subjects have been chosen (25 intact and four Amputees) to acquire the EMG data by these activities. To classify the recorded EMG data set, nine time domain and seven frequency domain features have been extracted. A comparative analysis of different classifiers is presented for different muscle position of electrodes. The signal processing and classification algorithms have been processed in MATLAB 2016a. The accuracy of classification ranges for different classification algorithms from 57.69% to 99.92% for all subjects. |
|---|---|
| AbstractList | Electromyography (EMG) signals are gaining popularity to develop the prosthetics. In this paper, an efficient multi-channel EMG signal acquisition system has been proposed for upper limb prosthetic application. Various arm exercises have been performed to obtain EMG signals from five different arm muscles for the validation of developed hardware. The muscle's position has been selected by palpation method. Furthermore, the classification algorithms have been examined for seven different activities. Total 29 subjects have been chosen (25 intact and four Amputees) to acquire the EMG data by these activities. To classify the recorded EMG data set, nine time domain and seven frequency domain features have been extracted. A comparative analysis of different classifiers is presented for different muscle position of electrodes. The signal processing and classification algorithms have been processed in MATLAB 2016a. The accuracy of classification ranges for different classification algorithms from 57.69% to 99.92% for all subjects. |
| Author | Pancholi, Sidharth Joshi, Amit M. |
| Author_xml | – sequence: 1 givenname: Sidharth surname: Pancholi fullname: Pancholi, Sidharth email: sid.2592@gmail.com organization: Dept. of Electron. & Commun. Eng., Malaviya Nat. Inst. of Technol., Jaipur, India – sequence: 2 givenname: Amit M. surname: Joshi fullname: Joshi, Amit M. email: amjoshi.ece@mnit.ac.in organization: Dept. of Electron. & Commun. Eng., Malaviya Nat. Inst. of Technol., Jaipur, India |
| BookMark | eNp9kMtOwzAQRS1UJNrCByA2_oGUcfyIs6xKKVQtVIJK7CLbcYRR2gTbXfD3JLRiwYLVjHTvGY3OCA32zd4idE1gQgjkt8uX-dMkBSInqYSccXmGhoRzmZCMyUG_U0gYzd4u0CiEDwCSZzwbouWm8VHp2uL5eoHvVFR4aj4PLrjomj1eN-Why6rG423bWo9XbqfxxjchvtvgAp62be2M6suX6LxSdbBXpzlG2_v56-whWT0vHmfTVWIoJTERRAvIpU0Fp5oqIKoELkSuBZWMQilKpQRJtRXAmBaiy1WZV5VkFTWmTOkYZce7pnsjeFsVxsWfD6JXri4IFL2SoldS9EqKk5KOJH_I1rud8l__MjdHxllrf_uSQso5od-_vW6P |
| CODEN | ISJEAZ |
| CitedBy_id | crossref_primary_10_1142_S0219519421500433 crossref_primary_10_1109_RBME_2019_2950897 crossref_primary_10_1002_aisy_202200063 crossref_primary_10_1080_03091902_2019_1653391 crossref_primary_10_3390_app112311199 crossref_primary_10_1109_JSEN_2021_3095118 crossref_primary_10_3390_s22155618 crossref_primary_10_1109_TNSRE_2022_3178384 crossref_primary_10_1109_TCYB_2020_3016595 crossref_primary_10_1587_elex_21_20240630 crossref_primary_10_1109_TIE_2019_2946536 crossref_primary_10_1177_09544119221074770 crossref_primary_10_1109_TMRB_2022_3216957 crossref_primary_10_1109_LSENS_2019_2906386 crossref_primary_10_34016_pjbt_2023_20_02_804 crossref_primary_10_1080_10255842_2023_2165068 crossref_primary_10_1109_TIM_2025_3545204 crossref_primary_10_1109_TIM_2022_3198443 crossref_primary_10_1016_j_medntd_2022_100165 crossref_primary_10_3390_s22165931 crossref_primary_10_1007_s00170_024_14803_1 crossref_primary_10_1109_MSP_2021_3057042 crossref_primary_10_1088_1757_899X_745_1_012094 crossref_primary_10_1109_JSEN_2022_3213868 crossref_primary_10_1007_s43154_020_00015_4 crossref_primary_10_3934_mbe_2021177 crossref_primary_10_1155_2019_3679174 crossref_primary_10_1007_s11517_023_02821_2 crossref_primary_10_3390_app10175823 crossref_primary_10_1007_s11517_021_02358_2 crossref_primary_10_1016_j_compeleceng_2025_110094 crossref_primary_10_1109_JSEN_2019_2931715 crossref_primary_10_1007_s13246_021_00972_w crossref_primary_10_1007_s13534_019_00130_y crossref_primary_10_1038_s41597_023_02223_x crossref_primary_10_1016_j_bspc_2020_102393 crossref_primary_10_1016_j_ish_2024_12_006 crossref_primary_10_3390_electronics8030259 crossref_primary_10_1109_TBCAS_2019_2914476 crossref_primary_10_1109_JSEN_2020_3042510 crossref_primary_10_3389_fnsys_2022_893275 crossref_primary_10_1016_j_bspc_2020_102122 crossref_primary_10_1109_LSENS_2019_2898257 crossref_primary_10_1016_j_patrec_2022_11_021 crossref_primary_10_1109_JSEN_2025_3529841 crossref_primary_10_1146_annurev_bioeng_082222_012531 crossref_primary_10_4015_S1016237224500236 crossref_primary_10_1155_2023_8231073 crossref_primary_10_1080_10739149_2019_1655441 crossref_primary_10_1016_j_sna_2021_112699 |
| Cites_doi | 10.1016/j.jvoice.2014.09.023 10.1109/TNSRE.2014.2305111 10.1016/j.cap.2010.11.051 10.1109/TNSRE.2010.2100828 10.1016/j.eswa.2012.01.102 10.1016/S1050-6411(03)00055-5 10.1109/MED.2014.6961396 10.1016/S1053-8135(97)00057-7 10.1109/86.481972 10.1007/s10916-014-0031-3 10.1109/10.923782 10.1109/TBME.1983.325162 10.1093/brain/awp135 10.1016/S1050-6411(97)00010-2 10.1007/s10916-015-0267-6 10.1109/TNSRE.2007.910282 10.1109/TLA.2016.7786339 10.1007/s12553-015-0113-3 10.1109/TBCAS.2013.2251343 10.1109/TSMCC.2009.2032660 10.1109/TBME.2003.813539 10.1016/S1050-6411(00)00050-X 10.1016/j.bspc.2007.07.009 10.1682/JRRD.2010.09.0177 10.1109/72.991427 10.1109/TBCAS.2015.2476555 10.1145/2702123.2702133 10.1186/1743-0003-3-27 10.1016/j.jelekin.2006.03.001 10.1016/j.jbiomech.2010.01.027 10.1109/TNSRE.2011.2108667 10.1016/S1352-8661(01)00145-4 10.1251/bpo115 10.1109/INDICON.2016.7839015 10.1007/s00422-008-0278-1 10.1109/JSEN.2015.2459067 |
| ContentType | Journal Article |
| DBID | 97E RIA RIE AAYXX CITATION |
| DOI | 10.1109/JSEN.2018.2809458 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geography Engineering |
| EISSN | 1558-1748 |
| EndPage | 3443 |
| ExternalDocumentID | 10_1109_JSEN_2018_2809458 8302551 |
| Genre | orig-research |
| GroupedDBID | -~X 0R~ 29I 4.4 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AGQYO AHBIQ AJQPL AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 EBS EJD F5P HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS TWZ AAYXX CITATION RIG |
| ID | FETCH-LOGICAL-c331t-61b6098e2653b3a01ad05669b638430d6daa612be6044b661adad9ff84f3ccd23 |
| IEDL.DBID | RIE |
| ISSN | 1530-437X |
| IngestDate | Thu Apr 24 23:10:14 EDT 2025 Tue Jul 01 03:36:41 EDT 2025 Wed Aug 27 03:05:16 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c331t-61b6098e2653b3a01ad05669b638430d6daa612be6044b661adad9ff84f3ccd23 |
| ORCID | 0000-0003-3450-9617 0000-0001-7919-1652 |
| PageCount | 8 |
| ParticipantIDs | crossref_citationtrail_10_1109_JSEN_2018_2809458 ieee_primary_8302551 crossref_primary_10_1109_JSEN_2018_2809458 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2018-April15,-15 2018-4-15 |
| PublicationDateYYYYMMDD | 2018-04-15 |
| PublicationDate_xml | – month: 04 year: 2018 text: 2018-April15,-15 day: 15 |
| PublicationDecade | 2010 |
| PublicationTitle | IEEE sensors journal |
| PublicationTitleAbbrev | JSEN |
| PublicationYear | 2018 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | ref35 ref13 ref34 ref12 ref15 ref36 ref14 houjie (ref24) 2012; 2 ref31 ref30 ref33 ref11 ref32 ref10 ref2 (ref39) 2017 ref1 ref17 ref38 ref16 ref19 (ref41) 2017 (ref18) 2010 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref8 ref7 (ref40) 2017 ref9 ref4 ref3 ref6 ref5 soo (ref37) 2011; 11 |
| References_xml | – ident: ref17 doi: 10.1016/j.jvoice.2014.09.023 – ident: ref26 doi: 10.1109/TNSRE.2014.2305111 – volume: 11 start-page: 740 year: 2011 ident: ref37 article-title: Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions publication-title: Current Appl Phys doi: 10.1016/j.cap.2010.11.051 – ident: ref21 doi: 10.1109/TNSRE.2010.2100828 – ident: ref23 doi: 10.1016/j.eswa.2012.01.102 – ident: ref31 doi: 10.1016/S1050-6411(03)00055-5 – ident: ref7 doi: 10.1109/MED.2014.6961396 – ident: ref32 doi: 10.1016/S1053-8135(97)00057-7 – ident: ref36 doi: 10.1109/86.481972 – ident: ref30 doi: 10.1007/s10916-014-0031-3 – year: 2017 ident: ref41 publication-title: NeXus-10-mkii – year: 2010 ident: ref18 publication-title: ADS129x Low-Power 8-Channel 24-Bit Analog Front-End for Biopotential Measurements Datasheet SBAS459K – ident: ref4 doi: 10.1109/10.923782 – ident: ref5 doi: 10.1109/TBME.1983.325162 – ident: ref38 doi: 10.1093/brain/awp135 – ident: ref2 doi: 10.1016/S1050-6411(97)00010-2 – year: 2017 ident: ref40 publication-title: MyoWare Muscle Sensor – ident: ref14 doi: 10.1007/s10916-015-0267-6 – ident: ref15 doi: 10.1109/TNSRE.2007.910282 – ident: ref20 doi: 10.1109/TLA.2016.7786339 – ident: ref12 doi: 10.1007/s12553-015-0113-3 – ident: ref13 doi: 10.1109/TBCAS.2013.2251343 – ident: ref22 doi: 10.1109/TSMCC.2009.2032660 – volume: 2 start-page: 12 year: 2012 ident: ref24 article-title: Portable biopotential signal acquisition system of ADS1298 anolog front-end publication-title: Microcontrollers and Embedded systems – ident: ref35 doi: 10.1109/TBME.2003.813539 – ident: ref3 doi: 10.1016/S1050-6411(00)00050-X – ident: ref25 doi: 10.1016/j.bspc.2007.07.009 – ident: ref6 doi: 10.1682/JRRD.2010.09.0177 – ident: ref16 doi: 10.1109/72.991427 – ident: ref28 doi: 10.1109/TBCAS.2015.2476555 – ident: ref27 doi: 10.1145/2702123.2702133 – ident: ref33 doi: 10.1186/1743-0003-3-27 – ident: ref34 doi: 10.1016/j.jelekin.2006.03.001 – ident: ref29 doi: 10.1016/j.jbiomech.2010.01.027 – ident: ref19 doi: 10.1109/TNSRE.2011.2108667 – ident: ref8 doi: 10.1016/S1352-8661(01)00145-4 – ident: ref1 doi: 10.1251/bpo115 – ident: ref10 doi: 10.1109/INDICON.2016.7839015 – ident: ref9 doi: 10.1007/s00422-008-0278-1 – year: 2017 ident: ref39 publication-title: PhysioLab Biosignal Module (Mini-Size Module With Isolation Power & Signal)-3 Types (PSL-iEMG 1-Channel EMG Anaglog Amplifier Module) – ident: ref11 doi: 10.1109/JSEN.2015.2459067 |
| SSID | ssj0019757 |
| Score | 2.4328325 |
| Snippet | Electromyography (EMG) signals are gaining popularity to develop the prosthetics. In this paper, an efficient multi-channel EMG signal acquisition system has... |
| SourceID | crossref ieee |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 3436 |
| SubjectTerms | Acquisition classification Electrodes Electromyography Feature extraction k-NN LDA Muscles prosthetic Prosthetics QDA sEMG Skin SVM Wrist |
| Title | Portable EMG Data Acquisition Module for Upper Limb Prosthesis Application |
| URI | https://ieeexplore.ieee.org/document/8302551 |
| Volume | 18 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3dT8IwEG-QF_XBD9CIX-mDT8aNbt3G-kgUJESIiZLwtvTjFokICNuD_vW225jEGOPb0rVJc9fmfte7-x1CV4YiBWQsLVeGyvJCoSzBwbHcmAsaSAJMmKeBwTDojbz-2B9X0E1ZCwMAWfIZ2OYzi-WruUzNU1nTcFX5pl56Sztuea1WGTFgrYzVU19gYnm0NS4imA5hzf5TZ2iSuELbDbU3Y7q7b9igjaYqmU3p7qPBejd5KsmrnSbClp8_iBr_u90DtFeAS9zOT8MhqsCshnY3KAdraLvoev7yUUf9LI1UTAF3Bvf4jicct-V7OsnTuPBgrlL9T6NaPFosYIkfJm8CP5oykRdYTVa4_R38PkKjbuf5tmcVvRUsSamTaI9RBISF4AY-FZQThysNhQIm9H30KFGB4lyDHwEB8TyhjThXXLE4Dr2YSqlceoyqs_kMThCOHQ0CGWsxpaGCBhQcfCdkYRC3YkPwBQ1E1tKOZEE8bvpfTKPMASEsMgqKjIKiQkENdF0uWeSsG39NrhvZlxMLsZ_-PnyGdsxiExBy_HNUTZYpXGhckYjL7EB9AZPnyEg |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LT8MwDLYQHIADb8SbHDghOtKm7ZrjBIMx1gkJJu1W5eFqE7ANaA_w60naMiaEELeqTarITuTPsf0Z4MRSpKBKleOpSDt-JLUjBbqOlwrJQkWRS3s1EHfDVs9v94P-HJxNa2EQsUg-w5p9LGL5eqxye1V2brmqAlsvvWDsfuCW1VrTmAGvF7ye5ghTx2f1fhXDdCk_b983uzaNK6p5kfFnbH_3GSs001alsCpXqxB_radMJnms5ZmsqY8fVI3_XfAarFTwkjTK_bAOczjagOUZ0sENWKz6ng_eN6FdJJLKJyTN-JpcikyQhnrJh2UiF4nHOjffDK4lvckEX0ln-CzJnS0UGeDb8I00vsPfW9C7aj5ctJyqu4KjGHMz4zPKkPIIvTBgkgnqCm3AUMilOZE-ozrUQhj4IzGkvi-NGRdaaJ6mkZ8ypbTHtmF-NB7hDpDUNTCQ8zrXBiwYSCEwcCMehWk9tRRfuAv0S9qJqqjHbQeMp6RwQShPrIISq6CkUtAunE6nTErejb8Gb1rZTwdWYt_7_fUxLLYe4k7Suene7sOS_ZEND7nBAcxnrzkeGpSRyaNic30CNrXLkQ |
| 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=Portable+EMG+Data+Acquisition+Module+for+Upper+Limb+Prosthesis+Application&rft.jtitle=IEEE+sensors+journal&rft.au=Pancholi%2C+Sidharth&rft.au=Joshi%2C+Amit+M.&rft.date=2018-04-15&rft.issn=1530-437X&rft.eissn=1558-1748&rft.volume=18&rft.issue=8&rft.spage=3436&rft.epage=3443&rft_id=info:doi/10.1109%2FJSEN.2018.2809458&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_JSEN_2018_2809458 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1530-437X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1530-437X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1530-437X&client=summon |