Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features
Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb...
Uložené v:
| Vydané v: | Neural networks Ročník 55; s. 42 - 58 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Kidlington
Elsevier Ltd
01.07.2014
Elsevier |
| Predmet: | |
| ISSN: | 0893-6080, 1879-2782, 1879-2782 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ≈8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available as a video supplement (see Appendix A).
•Human limb position has a substantial impact on the robustness of EMG pattern recognition.•Invariant power spectral moments described as a solution.•Real time classification experiments were carried out on 11 subjects.•Limb position invariant myoelectric pattern recognition achieved. |
|---|---|
| AbstractList | Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ≈8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available as a video supplement (see Appendix A).Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ≈8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available as a video supplement (see Appendix A). Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with approximately 8% approximately 8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available as a video supplement (see Appendix A). Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ≈8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available as a video supplement (see Appendix A). •Human limb position has a substantial impact on the robustness of EMG pattern recognition.•Invariant power spectral moments described as a solution.•Real time classification experiments were carried out on 11 subjects.•Limb position invariant myoelectric pattern recognition achieved. Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ≈8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available as a video supplement (see Appendix A). |
| Author | Khushaba, Rami N. Takruri, Maen Kodagoda, Sarath Miro, Jaime Valls |
| Author_xml | – sequence: 1 givenname: Rami N. surname: Khushaba fullname: Khushaba, Rami N. email: Rami.Khushaba@uts.edu.au organization: School of Electrical, Mechanical and Mechatronics Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia – sequence: 2 givenname: Maen surname: Takruri fullname: Takruri, Maen email: Maen.Takruri@aurak.edu.ae organization: American University of Ras Al Khaimah, Ras Al Khaimah, United Arab Emirates – sequence: 3 givenname: Jaime Valls surname: Miro fullname: Miro, Jaime Valls email: Jaime.VallsMiro@uts.edu.au organization: School of Electrical, Mechanical and Mechatronics Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia – sequence: 4 givenname: Sarath surname: Kodagoda fullname: Kodagoda, Sarath email: Sarath.Kodagoda@uts.edu.au organization: School of Electrical, Mechanical and Mechatronics Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28517036$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/24721224$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkk2L1TAYhYOMOHdG_4FIN4Kb1jdJm6YuBBn8ggE34zqk6ZshlzatSTrD_HtTegfBheMqEJ7nEE7OBTnzs0dCXlOoKFDx_lh5XD2migGtK-AVUHhGDlS2Xclayc7IAWTHSwESzslFjEcAELLmL8g5q1tGGasPxN7M9zoMsRjd1BfLHF1ysy-cv9PBaZ-K6WHGEU0KzhSLTgmDLwKa-dbv5Bqdvy2Sm7AccEE_YJbishl6LCzqtAaML8lzq8eIr07nJfn55fPN1bfy-sfX71efrkvTsDrltxq0KHopAXhjoWVgBZO0EYOgokNma2l6a_JVK5reaopWW6sHPnBouoFfknd77hLmXyvGpCYXDY6j9jivUVHRUgEtb_jTaFNDrqgW9X-gHLqGShAZfXNC137CQS3BTTo8qMfGM_D2BOho9GiD9sbFP5xsaAt8C_qwcybMMQa0yrikt8pzsW5UFNQ2A3VU-wzUNgMFXOUZZLn-S37Mf0L7uGuYv-jOYVDROPQGB5e_PKlhdv8O-A27zM8i |
| CitedBy_id | crossref_primary_10_1109_RBME_2019_2950897 crossref_primary_10_1109_ACCESS_2024_3373044 crossref_primary_10_1016_j_bspc_2019_101783 crossref_primary_10_1371_journal_pone_0291279 crossref_primary_10_3390_s19204596 crossref_primary_10_5370_KIEE_2016_65_1_194 crossref_primary_10_3390_su14095739 crossref_primary_10_1016_j_bspc_2019_101669 crossref_primary_10_3390_s22103737 crossref_primary_10_1007_s00202_025_02969_0 crossref_primary_10_1109_TBME_2022_3140269 crossref_primary_10_1016_j_measurement_2020_108456 crossref_primary_10_1109_JBHI_2022_3205058 crossref_primary_10_1016_j_rineng_2023_101660 crossref_primary_10_1166_jmihi_2021_3907 crossref_primary_10_1109_TIM_2023_3279873 crossref_primary_10_3233_JIFS_169794 crossref_primary_10_3390_s21217404 crossref_primary_10_1109_ACCESS_2021_3084442 crossref_primary_10_1007_s11004_021_09970_w crossref_primary_10_1109_TNSRE_2025_3604618 crossref_primary_10_1016_j_neucom_2017_11_072 crossref_primary_10_1109_JIOT_2025_3567890 crossref_primary_10_1109_TNSRE_2025_3602397 crossref_primary_10_3390_machines6040065 crossref_primary_10_1109_TNSRE_2020_2999505 crossref_primary_10_1088_1741_2552_ab4063 crossref_primary_10_1109_JSEN_2025_3570236 crossref_primary_10_3390_s24175828 crossref_primary_10_1016_j_cclet_2020_04_038 crossref_primary_10_1097_JPO_0000000000000121 crossref_primary_10_3390_app14083417 crossref_primary_10_1016_j_neucom_2016_05_038 crossref_primary_10_3389_fbioe_2022_1097363 crossref_primary_10_1016_j_bspc_2024_107403 crossref_primary_10_1109_LRA_2021_3097257 crossref_primary_10_3390_diagnostics11050843 crossref_primary_10_1109_JSEN_2019_2937979 crossref_primary_10_1016_j_compbiomed_2018_08_020 crossref_primary_10_3389_fresc_2025_1469797 crossref_primary_10_1109_TNSRE_2022_3218430 crossref_primary_10_3390_s19163548 crossref_primary_10_1109_JSEN_2022_3165988 crossref_primary_10_1109_TBCAS_2023_3314053 crossref_primary_10_1098_rsta_2021_0268 crossref_primary_10_1038_s41597_024_04296_8 crossref_primary_10_3389_fnins_2023_1158280 crossref_primary_10_1109_TNSRE_2022_3141593 crossref_primary_10_3389_fbioe_2022_876836 crossref_primary_10_1088_1741_2560_11_5_051001 crossref_primary_10_1109_TIM_2024_3373045 crossref_primary_10_1080_00051144_2019_1565337 crossref_primary_10_1109_ACCESS_2018_2851282 crossref_primary_10_1109_TNSRE_2015_2478138 crossref_primary_10_1155_2021_5511922 crossref_primary_10_1109_TIE_2015_2497212 crossref_primary_10_1088_1757_899X_671_1_012064 crossref_primary_10_1109_JSEN_2023_3347949 crossref_primary_10_1109_TNSRE_2020_2991643 crossref_primary_10_3389_fnins_2021_657958 crossref_primary_10_3390_s22052007 crossref_primary_10_1109_JSEN_2024_3441102 crossref_primary_10_1371_journal_pone_0186318 crossref_primary_10_1007_s00221_018_5441_x crossref_primary_10_1109_TNSRE_2019_2907200 crossref_primary_10_3390_s22135005 crossref_primary_10_1088_1742_6596_2008_1_012015 crossref_primary_10_1016_j_bspc_2019_101626 crossref_primary_10_1016_j_bspc_2020_101872 crossref_primary_10_1177_09544119221074770 crossref_primary_10_1016_j_bbe_2017_11_001 crossref_primary_10_1016_j_cmpb_2019_105278 crossref_primary_10_1109_ACCESS_2020_3000357 crossref_primary_10_1109_TNSRE_2015_2445634 crossref_primary_10_3389_fnbot_2018_00058 crossref_primary_10_1016_j_bspc_2016_01_011 crossref_primary_10_1038_s41598_024_54677_7 crossref_primary_10_1109_JSEN_2023_3344700 crossref_primary_10_7717_peerj_cs_949 crossref_primary_10_1088_1741_2552_adf888 crossref_primary_10_1177_09544119211053669 crossref_primary_10_1016_j_medengphy_2015_02_005 crossref_primary_10_1088_1741_2552_ac7079 crossref_primary_10_1109_TNSRE_2017_2687520 crossref_primary_10_1007_s00521_020_05536_9 crossref_primary_10_1088_1741_2552_ab673f crossref_primary_10_1109_JSEN_2025_3577610 crossref_primary_10_3390_sym12101710 crossref_primary_10_1109_TNSRE_2023_3237181 crossref_primary_10_3390_s22010225 crossref_primary_10_1016_j_bbe_2021_03_006 crossref_primary_10_3389_fneur_2017_00007 crossref_primary_10_1088_1741_2552_abbed0 crossref_primary_10_3390_s18051615 crossref_primary_10_1080_10255842_2022_2054271 crossref_primary_10_1109_TIM_2022_3141163 crossref_primary_10_3389_frobt_2021_710806 crossref_primary_10_1016_j_birob_2025_100250 crossref_primary_10_3390_s25185920 crossref_primary_10_1016_j_artmed_2020_102005 crossref_primary_10_1016_j_eswa_2016_05_031 crossref_primary_10_1016_j_aca_2021_339223 crossref_primary_10_1016_j_bspc_2018_02_006 crossref_primary_10_1016_j_compbiomed_2017_09_013 crossref_primary_10_3390_bdcc2030021 crossref_primary_10_1016_j_bspc_2018_02_013 crossref_primary_10_1109_ACCESS_2022_3166885 crossref_primary_10_1109_TNSRE_2015_2481461 crossref_primary_10_1109_TNSRE_2020_3022587 crossref_primary_10_3390_computers7040058 crossref_primary_10_3389_fnins_2025_1568212 crossref_primary_10_3389_fnbot_2019_00043 crossref_primary_10_1038_s44182_025_00018_3 crossref_primary_10_1080_10255842_2024_2310726 crossref_primary_10_1088_1742_6596_1373_1_012051 crossref_primary_10_1371_journal_pone_0321319 crossref_primary_10_1155_2022_6414664 crossref_primary_10_3389_frai_2021_744476 crossref_primary_10_1016_j_compbiomed_2020_104188 crossref_primary_10_3390_s20061613 crossref_primary_10_1109_TNSRE_2018_2861465 crossref_primary_10_3390_robotics14060083 |
| Cites_doi | 10.1016/j.bspc.2007.07.009 10.1007/s11517-007-0291-x 10.1109/IEMBS.2010.5627638 10.1109/TNSRE.2012.2196711 10.1109/MEMB.2002.1044184 10.1145/1357054.1357138 10.1007/s11517-006-0100-y 10.1109/18.53742 10.1046/j.1365-201X.1998.0298f.x 10.1109/TBME.2006.889175 10.1109/TSA.2005.851998 10.1097/JPO.0b013e3182524cce 10.1109/70.538982 10.1109/10.204774 10.1109/TNSRE.2007.891391 10.1109/TBME.1986.325697 10.1155/ASP.2005.3165 10.1109/TNSRE.2011.2163529 10.1109/TBME.2005.856295 10.1109/ISSNIP.2011.6146521 10.1109/10.821766 10.1016/0141-5425(82)90021-8 10.1109/TCSI.2005.857555 10.1016/j.eswa.2012.02.192 10.1109/10.914793 10.1109/TBME.2011.2113182 10.1088/0967-3334/24/2/307 10.1109/TBME.2009.2039480 10.1109/ISCIT.2007.4392044 10.1109/CISP.2011.6100025 10.1016/j.bspc.2007.11.005 10.1016/0013-4694(70)90143-4 10.1109/TBME.2008.2003293 10.1109/TBME.2012.2191551 10.1109/TNSRE.2009.2039590 10.1016/j.eswa.2010.09.068 10.1109/BHI.2012.6211702 10.1109/TSMCB.2011.2168604 10.1109/86.481972 10.1109/TBME.2003.813539 10.1109/TBME.2008.919734 10.1007/BF00421659 10.1016/0013-4694(90)90015-C 10.1109/IEMBS.2006.4397932 10.1145/1753326.1753451 10.1109/TBME.2007.909536 10.1016/j.eswa.2012.01.102 |
| ContentType | Journal Article |
| Copyright | 2014 Elsevier Ltd 2015 INIST-CNRS Copyright © 2014 Elsevier Ltd. All rights reserved. |
| Copyright_xml | – notice: 2014 Elsevier Ltd – notice: 2015 INIST-CNRS – notice: Copyright © 2014 Elsevier Ltd. All rights reserved. |
| DBID | AAYXX CITATION IQODW CGR CUY CVF ECM EIF NPM 7X8 7TK 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.neunet.2014.03.010 |
| DatabaseName | CrossRef Pascal-Francis Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic Neurosciences Abstracts Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic Neurosciences Abstracts Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | MEDLINE - Academic Neurosciences Abstracts MEDLINE Computer and Information Systems Abstracts |
| 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 | fulltext_linktorsrc |
| Discipline | Computer Science Applied Sciences |
| EISSN | 1879-2782 |
| EndPage | 58 |
| ExternalDocumentID | 24721224 28517036 10_1016_j_neunet_2014_03_010 S0893608014000732 |
| Genre | Clinical Trial Journal Article |
| GroupedDBID | --- --K --M -~X .DC .~1 0R~ 123 186 1B1 1RT 1~. 1~5 29N 4.4 457 4G. 53G 5RE 5VS 6TJ 7-5 71M 8P~ 9JM 9JN AABNK AACTN AADPK AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXLA AAXUO AAYFN ABAOU ABBOA ABCQJ ABEFU ABFNM ABFRF ABHFT ABIVO ABJNI ABLJU ABMAC ABXDB ABYKQ ACAZW ACDAQ ACGFO ACGFS ACIUM ACNNM ACRLP ACZNC ADBBV ADEZE ADGUI ADJOM ADMUD ADRHT AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ARUGR ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F0J F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q G8K GBLVA GBOLZ HLZ HMQ HVGLF HZ~ IHE J1W JJJVA K-O KOM KZ1 LG9 LMP M2V M41 MHUIS MO0 MOBAO MVM N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SCC SDF SDG SDP SES SEW SNS SPC SPCBC SSN SST SSV SSW SSZ T5K TAE UAP UNMZH VOH WUQ XPP ZMT ~G- 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD BNPGV IQODW SSH AGCQF AGRNS CGR CUY CVF ECM EIF NPM 7X8 7TK 7SC 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c524t-60cefe6b880035f0720f628156d6169e2f48cbfc281765bfa1efaffad3d3059d3 |
| ISICitedReferencesCount | 162 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000337860600005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0893-6080 1879-2782 |
| IngestDate | Thu Oct 02 16:28:31 EDT 2025 Thu Sep 25 08:38:03 EDT 2025 Sun Sep 28 00:21:43 EDT 2025 Mon Jul 21 06:08:12 EDT 2025 Wed Apr 02 07:16:10 EDT 2025 Tue Nov 18 20:44:40 EST 2025 Sat Nov 29 07:59:01 EST 2025 Fri Feb 23 02:28:38 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Electromyogram (EMG) Signal processing Spectral moments Invariant Correlation Fourier transformation Prosthesis Irregularity Video signal Modeling Time dependence Signal spectrum Classification Size effect Selection criterion Electromyography Medical application Pattern extraction Video technique Pattern recognition Real time Time domain method Feature extraction Data gathering Power spectrum |
| Language | English |
| License | CC BY 4.0 Copyright © 2014 Elsevier Ltd. All rights reserved. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c524t-60cefe6b880035f0720f628156d6169e2f48cbfc281765bfa1efaffad3d3059d3 |
| Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
| PMID | 24721224 |
| PQID | 1530951806 |
| PQPubID | 23479 |
| PageCount | 17 |
| ParticipantIDs | proquest_miscellaneous_1671607353 proquest_miscellaneous_1540224464 proquest_miscellaneous_1530951806 pubmed_primary_24721224 pascalfrancis_primary_28517036 crossref_citationtrail_10_1016_j_neunet_2014_03_010 crossref_primary_10_1016_j_neunet_2014_03_010 elsevier_sciencedirect_doi_10_1016_j_neunet_2014_03_010 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-07-01 |
| PublicationDateYYYYMMDD | 2014-07-01 |
| PublicationDate_xml | – month: 07 year: 2014 text: 2014-07-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Kidlington |
| PublicationPlace_xml | – name: Kidlington – name: United States |
| PublicationTitle | Neural networks |
| PublicationTitleAlternate | Neural Netw |
| PublicationYear | 2014 |
| Publisher | Elsevier Ltd Elsevier |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier |
| References | Du, S. 2003. Feature extraction for classification prehensile Electromyography patterns Scheme, Englehart, Hudgins (br000270) 2011; 58 Graupe, Salahi, Kohn (br000115) 1982; 4 Basmajian, De Luca (br000005) 1985 Shenoy, Miller, Crawford, Rao (br000285) 2008; 55 CHI, April 5–10, Florence, Italy. Zhang, Zhou (br000335) 2012; 59 BHI (pp. 788–791). Lock, B.A. 2005. Design and interactive assessment of continuous multifunction myoelectric control systems (pp. 2417–2420), New York City, USA. Fougner, Scheme, Chan, Englehart, Stavdahl (br000090) 2011; 19 Lin, Wu, Jung, Liang, Huang (br000200) 2005; 2005 Hudgins, Parker, Scott (br000155) 1993; 40 Khushaba, R.N., Kodagoa, S., Liu, D., & Dissanayake, G. 2011. Electromyogram (EMG) based fingers movement recognition using neighborhood preserving analysis with QR-decomposition. In Hannaford, Lehman (br000120) 1986; BME-33 (pp. 344–350). Lin, Wu, Liang, Chao, Chen, Jung (br000205) 2005; 52 Phinyomark, Limsakul, Phukpattaranont (br000240) 2009; 1 August 23–26, HeFei, China (pp. 1063–1072). Yan, Wang, Xie (br000325) 2008; 46 Farrell, Weir (br000075) 2007; 15 Oskoei, Hu (br000225) 2007; 2 Wang, Wang, Chen, Zhuang (br000315) 2007; 44 Huang, Englehart, Hudgins, Chan (br000140) 2005; 52 IJCNN pp. 5294–5300. Chen, L., Geng, Y., & Li, G. 2011. Effect of upper-limb positions on motion pattern recognition using electromyography. In . CMBEC28, Quebec City, Canada (pp. 141–144). Khushaba, Al-Ani, Al-Jumaily (br000175) 2010; 57 Huang, Kuiken, Lipschutz (br000145) 2009; 56 Phinyomark, A., Hirunviriya, S., Limsakul, C., & Phukpattaranont, P. 2010. Evaluation of EMG feature extraction for hand movement recognition based on Euclidean distance and standard deviation. In Khushaba, Kodagoda, Takruri, Dissanayake (br000190) 2012; 39 Oskoei, Hu (br000230) 2008; 55 Hargrove, Englehart, Hudgins (br000125) 2008; 3 ECTI-CON (pp. 856–860). Bello, Daudet, Abdallah, Duxbury, Davies, Sandler (br000010) 2005; 13 Kay (br000170) 1998 Jiang, Dosen, Muller, Farina (br000160) 2012; 29 Adelaide, Australia (pp. 100–105). Dirlik, T. 1985. Application of computers in fatigue Merletti, Parker (br000220) 2004 Smith (br000290) 2007 Goncharova, Barlow (br000110) 1990; 76 Huang, Zhou, Ding, Zhang (br000150) 2012; 42 Matsumura, Y., Fukumi, M., & Mitsukura, Y. 2006. Hybrid EMG Recognition System by MDA and PCA. In Boostani, Moradi (br000015) 2003; 24 Rafiee, Rafiee, Yavari, Schoen (br000250) 2011; 38 Phinyomark, Phukpattaranont, Limsakul (br000245) 2012; 39 Englehart, Hudgins (br000065) 2003; 50 Chu, J.U., Moon, I., & Mun, M.S. 2006. A supervised feature projection for real-time multifunction myoelectric hand control. In Zardoshti-Kermani, Wheeler, Badie, Hashemi (br000330) 1995; 3 Saponas, T.S., Tan, D.S., Morris, D., Turner, J., & Landay, J.A. 2010. Making muscle–computer interfaces more practical. In Du, S., & Vuskovic, M. 2004. Temporal vs. spectral approach to feature extraction from prehensile EMG signals. In Buenos Aires, Sep. (pp. 6337–6340). Englehart, K. 1998. Signal representation for classification of the transient myoelectric signal Tkach, Huang, Kuiken (br000300) 2010; 7 (pp. 139–142). Theodoridis, Koutroumbas (br000295) 2009 Vuskovic, M., & Du, S. 2005. Spectral moments for feature extraction from temporal signals. In Englehart, Hudgins, Parker (br000070) 2001; 48 Scheme, Hudgins, Parker (br000280) 2007; 54 Hargrove, Scheme, Englehart, Hudgins (br000130) 2010; 18 Karlsson, Jun, Akay (br000165) 2000; 47 Li (br000195) 2011 Goge, A.R., & Chan, A.D.C. 2004. Investigating classification parameters for continuous myoelectrically controlled prostheses. In Saponas, T.S., Tan, D.S., Morris, D., & Balakrishnan, R. 2008. Demonstrating the feasibility of using forearm electromyography for muscle–computer interfaces. In White, Boashash (br000320) 1990; 36 Fougner, Stavdahl, Kyberd, Losier, Parker (br000095) 2012 Sahlin, Tonkonogi, Soderlund (br000255) 1998; 162 Geng, Y., Chen, L., Tian, L., & Li, G. 2012. Comparison of electromyography and mechanomyogram in control of prosthetic system in multiple limb positions. In Vigreux, Cnockaert, Pertuzon (br000305) 1979; 41 Cipriani, Controzzi, Kanitz, Sassu (br000040) 2012; 24 Khushaba, R.N., Al-Jumaily, A., & Al-Ani, A. 2007. Novel feature extraction method based on fuzzy entropy and wavelet packet transform for myoelectric control. In CHI, April 10–15, Atlanta, Georgia, USA. Scheme, E., Fougner, A., Chan, A.D.C., Stavdahl, O., & Englehart, K. 2010. Examining the adverse effects of limb position on pattern recognition based myoelectric control. In Flusser, Suk, Zitova (br000085) 2009 Hjorth (br000135) 1970; 29 Chan, Englehart, Hudgins, Lovely (br000020) 2002; 21 Chu, Moon, Kim, Mun (br000030) 2005; IROS ISCIT (pp. 352–357). Farry, Walker, Baraniuk (br000080) 1996; 12 Hannaford (10.1016/j.neunet.2014.03.010_br000120) 1986; BME-33 10.1016/j.neunet.2014.03.010_br000035 Tkach (10.1016/j.neunet.2014.03.010_br000300) 2010; 7 10.1016/j.neunet.2014.03.010_br000310 Phinyomark (10.1016/j.neunet.2014.03.010_br000245) 2012; 39 10.1016/j.neunet.2014.03.010_br000275 Rafiee (10.1016/j.neunet.2014.03.010_br000250) 2011; 38 Khushaba (10.1016/j.neunet.2014.03.010_br000175) 2010; 57 Huang (10.1016/j.neunet.2014.03.010_br000140) 2005; 52 Chan (10.1016/j.neunet.2014.03.010_br000020) 2002; 21 Englehart (10.1016/j.neunet.2014.03.010_br000065) 2003; 50 10.1016/j.neunet.2014.03.010_br000235 Bello (10.1016/j.neunet.2014.03.010_br000010) 2005; 13 Sahlin (10.1016/j.neunet.2014.03.010_br000255) 1998; 162 Fougner (10.1016/j.neunet.2014.03.010_br000090) 2011; 19 Lin (10.1016/j.neunet.2014.03.010_br000200) 2005; 2005 Basmajian (10.1016/j.neunet.2014.03.010_br000005) 1985 10.1016/j.neunet.2014.03.010_br000045 Hargrove (10.1016/j.neunet.2014.03.010_br000125) 2008; 3 Huang (10.1016/j.neunet.2014.03.010_br000145) 2009; 56 Khushaba (10.1016/j.neunet.2014.03.010_br000190) 2012; 39 Yan (10.1016/j.neunet.2014.03.010_br000325) 2008; 46 Zardoshti-Kermani (10.1016/j.neunet.2014.03.010_br000330) 1995; 3 Zhang (10.1016/j.neunet.2014.03.010_br000335) 2012; 59 Theodoridis (10.1016/j.neunet.2014.03.010_br000295) 2009 Scheme (10.1016/j.neunet.2014.03.010_br000280) 2007; 54 Kay (10.1016/j.neunet.2014.03.010_br000170) 1998 Shenoy (10.1016/j.neunet.2014.03.010_br000285) 2008; 55 Hargrove (10.1016/j.neunet.2014.03.010_br000130) 2010; 18 Scheme (10.1016/j.neunet.2014.03.010_br000270) 2011; 58 Cipriani (10.1016/j.neunet.2014.03.010_br000040) 2012; 24 10.1016/j.neunet.2014.03.010_br000210 10.1016/j.neunet.2014.03.010_br000055 Goncharova (10.1016/j.neunet.2014.03.010_br000110) 1990; 76 Hjorth (10.1016/j.neunet.2014.03.010_br000135) 1970; 29 Oskoei (10.1016/j.neunet.2014.03.010_br000225) 2007; 2 10.1016/j.neunet.2014.03.010_br000050 Flusser (10.1016/j.neunet.2014.03.010_br000085) 2009 Huang (10.1016/j.neunet.2014.03.010_br000150) 2012; 42 Smith (10.1016/j.neunet.2014.03.010_br000290) 2007 Li (10.1016/j.neunet.2014.03.010_br000195) 2011 10.1016/j.neunet.2014.03.010_br000215 Farry (10.1016/j.neunet.2014.03.010_br000080) 1996; 12 Farrell (10.1016/j.neunet.2014.03.010_br000075) 2007; 15 Chu (10.1016/j.neunet.2014.03.010_br000030) 2005; IROS Phinyomark (10.1016/j.neunet.2014.03.010_br000240) 2009; 1 White (10.1016/j.neunet.2014.03.010_br000320) 1990; 36 Graupe (10.1016/j.neunet.2014.03.010_br000115) 1982; 4 Jiang (10.1016/j.neunet.2014.03.010_br000160) 2012; 29 10.1016/j.neunet.2014.03.010_br000180 10.1016/j.neunet.2014.03.010_br000060 Wang (10.1016/j.neunet.2014.03.010_br000315) 2007; 44 10.1016/j.neunet.2014.03.010_br000100 10.1016/j.neunet.2014.03.010_br000265 Boostani (10.1016/j.neunet.2014.03.010_br000015) 2003; 24 Vigreux (10.1016/j.neunet.2014.03.010_br000305) 1979; 41 10.1016/j.neunet.2014.03.010_br000185 10.1016/j.neunet.2014.03.010_br000260 Englehart (10.1016/j.neunet.2014.03.010_br000070) 2001; 48 10.1016/j.neunet.2014.03.010_br000105 Merletti (10.1016/j.neunet.2014.03.010_br000220) 2004 10.1016/j.neunet.2014.03.010_br000025 Fougner (10.1016/j.neunet.2014.03.010_br000095) 2012 Karlsson (10.1016/j.neunet.2014.03.010_br000165) 2000; 47 Lin (10.1016/j.neunet.2014.03.010_br000205) 2005; 52 Oskoei (10.1016/j.neunet.2014.03.010_br000230) 2008; 55 Hudgins (10.1016/j.neunet.2014.03.010_br000155) 1993; 40 |
| References_xml | – volume: 46 start-page: 519 year: 2008 end-page: 527 ident: br000325 article-title: Joint application of rough set-based feature reduction and fuzzy LS-SVM classifier in motion classification publication-title: Medical and Biological Engineering and Computing – volume: 48 start-page: 302 year: 2001 end-page: 311 ident: br000070 article-title: A wavelet-based continuous classification scheme for multifunction myoelectric control publication-title: IEEE Transactions on Biomedical Engineering – volume: 19 start-page: 644 year: 2011 end-page: 651 ident: br000090 article-title: Resolving the limb position effect in myoelectric pattern recognition publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering – year: 2012 ident: br000095 article-title: Control of upper limb prostheses: terminology and proportional myoelectric control—a review publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering – year: 2004 ident: br000220 article-title: Electromyography physiology, engineering, and noninvasive applications publication-title: IEEE press series in biomedical engineering – volume: 3 start-page: 324 year: 1995 end-page: 333 ident: br000330 article-title: EMG feature evaluation for movement control of upper extremity prostheses publication-title: IEEE Transactions on Rehabilitation Engineering – reference: Chu, J.U., Moon, I., & Mun, M.S. 2006. A supervised feature projection for real-time multifunction myoelectric hand control. In – reference: (pp. 139–142). – volume: 55 year: 2008 ident: br000230 article-title: Support vector machine-based classification scheme for myoelectric control applied to upper limb publication-title: IEEE Transactions on Biomedical Engineering – volume: 42 start-page: 513 year: 2012 end-page: 529 ident: br000150 article-title: Extreme learning machine for regression and multiclass classification publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics – volume: 76 start-page: 197 year: 1990 end-page: 204 ident: br000110 article-title: Changes in EEG mean frequency and spectral purity during spontaneous alpha blocking publication-title: Electroencephalography and Clinical Neurophysiology – reference: Vuskovic, M., & Du, S. 2005. Spectral moments for feature extraction from temporal signals. In – volume: IROS start-page: 3511 year: 2005 end-page: 3516 ident: br000030 article-title: Control of multifunction myoelectric hand using a real-time EMG pattern recognition publication-title: EEE/RSJ International Conference on Intelligent Robots and Systems – reference: (pp. 344–350). – reference: Saponas, T.S., Tan, D.S., Morris, D., & Balakrishnan, R. 2008. Demonstrating the feasibility of using forearm electromyography for muscle–computer interfaces. In – reference: , Buenos Aires, Sep. (pp. 6337–6340). – volume: 12 start-page: 775 year: 1996 end-page: 787 ident: br000080 article-title: Myoelectric teleoperation of a complex robotic hand publication-title: IEEE Transactions on Robotics and Automation – year: 1985 ident: br000005 article-title: Muscles alive – volume: 52 start-page: 1801 year: 2005 end-page: 1811 ident: br000140 article-title: A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses publication-title: IEEE Transactions on Biomedical Engineering – start-page: 99 year: 2011 end-page: 116 ident: br000195 article-title: Electromyography pattern-recognition-based control of powered multifunctional upper-limb prostheses publication-title: Advances in applied electromyography – volume: 52 start-page: 2726 year: 2005 end-page: 2738 ident: br000205 article-title: EEG-based drowsiness estimation for safety driving using independent component analysis publication-title: IEEE Transactions on Circuits and Systems- I, Reg. Papers – reference: (pp. 2417–2420), New York City, USA. – volume: 15 start-page: 111 year: 2007 end-page: 118 ident: br000075 article-title: The optimal controller delay for myoelectric prostheses publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering – volume: 29 start-page: 148 year: 2012 end-page: 152 ident: br000160 article-title: Myoelectric control of artificial limbs—is there a need to change focus? publication-title: IEEE Signal Processing Magazine – reference: Phinyomark, A., Hirunviriya, S., Limsakul, C., & Phukpattaranont, P. 2010. Evaluation of EMG feature extraction for hand movement recognition based on Euclidean distance and standard deviation. In – reference: , CMBEC28, Quebec City, Canada (pp. 141–144). – reference: Goge, A.R., & Chan, A.D.C. 2004. Investigating classification parameters for continuous myoelectrically controlled prostheses. In – volume: 39 start-page: 7420 year: 2012 end-page: 7731 ident: br000245 article-title: Feature reduction and selection for EMG signal classification publication-title: Expert Systems with Applications – reference: , August 23–26, HeFei, China (pp. 1063–1072). – reference: Matsumura, Y., Fukumi, M., & Mitsukura, Y. 2006. Hybrid EMG Recognition System by MDA and PCA. In – volume: 36 start-page: 830 year: 1990 end-page: 835 ident: br000320 article-title: Cross spectral analysis of non-stationary processes publication-title: IEEE Transactions on Information Theory – reference: Du, S., & Vuskovic, M. 2004. Temporal vs. spectral approach to feature extraction from prehensile EMG signals. In – reference: , IJCNN pp. 5294–5300. – year: 2007 ident: br000290 article-title: Mathematics of the discrete Fourier transform (DFT) with audio applications – volume: 56 start-page: 65 year: 2009 end-page: 73 ident: br000145 article-title: A strategy for identifying locomotion modes using surface electromyography publication-title: IEEE Transactions on Biomedical Engineering – reference: Du, S. 2003. Feature extraction for classification prehensile Electromyography patterns, – volume: 47 start-page: 228 year: 2000 end-page: 238 ident: br000165 article-title: Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study publication-title: IEEE Transactions on Biomedical Engineering – volume: 2 start-page: 275 year: 2007 end-page: 294 ident: br000225 article-title: Myoelectric control systems—a survey publication-title: Biomedical Signal Processing and Control – volume: 44 start-page: 865 year: 2007 end-page: 872 ident: br000315 article-title: Classification of surface EMG signals using optimal wavelet packet method based on Davies–Bouldin criterion publication-title: Medical and Biological Engineering and Computing – volume: 162 start-page: 261 year: 1998 end-page: 266 ident: br000255 article-title: Energy supply and muscle fatigue in humans publication-title: Acta Physiologica Scandinavica – volume: 59 start-page: 1649 year: 2012 end-page: 1657 ident: br000335 article-title: High-density myoelectric pattern recognition toward improved stroke rehabilitation publication-title: IEEE Transactions on Biomedical Engineering – reference: Lock, B.A. 2005. Design and interactive assessment of continuous multifunction myoelectric control systems, – reference: , CHI, April 10–15, Atlanta, Georgia, USA. – volume: 18 start-page: 49 year: 2010 end-page: 57 ident: br000130 article-title: Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering – volume: 41 start-page: 119 year: 1979 end-page: 129 ident: br000305 article-title: Factors influencing quantified surface EMGs publication-title: European Journal of Applied Physiology and Occupational Physiology – volume: 57 start-page: 1410 year: 2010 end-page: 1419 ident: br000175 article-title: Orthogonal fuzzy neighborhood discriminant analysis for multifunction myoelectric hand control publication-title: IEEE Transactions on Biomedical Engineering – volume: 54 start-page: 694 year: 2007 end-page: 699 ident: br000280 article-title: Myoelectric signal classification for phoneme-based speech recognition publication-title: IEEE Transactions on Biomedical Engineering – reference: Khushaba, R.N., Kodagoa, S., Liu, D., & Dissanayake, G. 2011. Electromyogram (EMG) based fingers movement recognition using neighborhood preserving analysis with QR-decomposition. In – volume: 1 start-page: 2151 year: 2009 end-page: 9617 ident: br000240 article-title: A novel feature extraction for robust EMG pattern recognition publication-title: Journal of Computing – reference: , CHI, April 5–10, Florence, Italy. – volume: 24 start-page: 309 year: 2003 end-page: 319 ident: br000015 article-title: Evaluation of the forearm EMG signal features for the control of a prosthetic hand publication-title: Physiological Measurement – reference: Scheme, E., Fougner, A., Chan, A.D.C., Stavdahl, O., & Englehart, K. 2010. Examining the adverse effects of limb position on pattern recognition based myoelectric control. In – reference: Englehart, K. 1998. Signal representation for classification of the transient myoelectric signal, – reference: Khushaba, R.N., Al-Jumaily, A., & Al-Ani, A. 2007. Novel feature extraction method based on fuzzy entropy and wavelet packet transform for myoelectric control. In – reference: , ISCIT (pp. 352–357). – reference: , Adelaide, Australia (pp. 100–105). – volume: 39 start-page: 10731 year: 2012 end-page: 10738 ident: br000190 article-title: Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals publication-title: Expert Systems with Applications – volume: 38 start-page: 4058 year: 2011 end-page: 4067 ident: br000250 article-title: Feature extraction of forearm EMG signals for prosthetics publication-title: Expert Systems with Applications – volume: 2005 start-page: 3165 year: 2005 end-page: 3174 ident: br000200 article-title: Estimating alertness level based on EEG spectrum analysis publication-title: EURASIP Journal of Applied Signal Processing – reference: Chen, L., Geng, Y., & Li, G. 2011. Effect of upper-limb positions on motion pattern recognition using electromyography. In – reference: Dirlik, T. 1985. Application of computers in fatigue, – year: 2009 ident: br000295 article-title: Pattern recognition – year: 2009 ident: br000085 article-title: Moments and moment invariants in pattern recognition – volume: 55 start-page: 1128 year: 2008 end-page: 1135 ident: br000285 article-title: Online electromyographic control of a robotic prosthesis publication-title: IEEE Transactions on Biomedical Engineering – volume: 4 start-page: 17 year: 1982 end-page: 22 ident: br000115 article-title: Multifunctional prosthesis and orthosis control via microcomputer identification of temporal pattern differences in single-site myoelectric signals publication-title: Journal of Biomedical Engineering – reference: , ECTI-CON (pp. 856–860). – volume: 58 start-page: 1698 year: 2011 end-page: 1705 ident: br000270 article-title: Selective classification for improved robustness of myoelectric control under nonideal conditions publication-title: IEEE Transactions on Biomedical Engineering – reference: Saponas, T.S., Tan, D.S., Morris, D., Turner, J., & Landay, J.A. 2010. Making muscle–computer interfaces more practical. In – reference: . – year: 1998 ident: br000170 article-title: Modern spectral estimation: theory and application – volume: 7 year: 2010 ident: br000300 article-title: Study of stability of time-domain features for electromyographic pattern recognition publication-title: Journal of NeuroEngineering and Rehabilitation – volume: 50 start-page: 848 year: 2003 end-page: 854 ident: br000065 article-title: A robust, real-time control scheme for multifunction myoelectric control publication-title: IEEE Transactions on Biomedical Engineering – volume: 29 start-page: 306 year: 1970 end-page: 310 ident: br000135 article-title: EEG analysis based on time domain properties publication-title: Electroencephalography and Clinical Neurophysiology – volume: BME-33 start-page: 1173 year: 1986 end-page: 1181 ident: br000120 article-title: Short time Fourier analysis of the electromyogram: fast movements and constant contraction publication-title: IEEE Transactions on Biomedical Engineering – volume: 24 start-page: 86 year: 2012 end-page: 92 ident: br000040 article-title: The effects of weight and inertia of the prosthesis on the sensitivity of electromyographic pattern recognition in relax state publication-title: Journal of Prosthetics and Orthotics – reference: Geng, Y., Chen, L., Tian, L., & Li, G. 2012. Comparison of electromyography and mechanomyogram in control of prosthetic system in multiple limb positions. In – volume: 13 start-page: 1035 year: 2005 end-page: 1047 ident: br000010 article-title: A tutorial on onset detection in music signals publication-title: IEEE Transactions on Speech and Audio Processing – volume: 21 start-page: 143 year: 2002 end-page: 146 ident: br000020 article-title: Hidden Markov model classification of myoelectric signals in speech publication-title: IEEE Engineering in Medicine and Biology Magazine – reference: , BHI (pp. 788–791). – volume: 3 start-page: 175 year: 2008 end-page: 180 ident: br000125 article-title: A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control publication-title: Biomedical Signal Processing and Control – volume: 40 start-page: 82 year: 1993 end-page: 94 ident: br000155 article-title: A new strategy for multifunction myoelectric control publication-title: IEEE Transactions on Biomedical Engineering – volume: 2 start-page: 275 issue: 4 year: 2007 ident: 10.1016/j.neunet.2014.03.010_br000225 article-title: Myoelectric control systems—a survey publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2007.07.009 – volume: 46 start-page: 519 issue: 6 year: 2008 ident: 10.1016/j.neunet.2014.03.010_br000325 article-title: Joint application of rough set-based feature reduction and fuzzy LS-SVM classifier in motion classification publication-title: Medical and Biological Engineering and Computing doi: 10.1007/s11517-007-0291-x – ident: 10.1016/j.neunet.2014.03.010_br000275 doi: 10.1109/IEMBS.2010.5627638 – year: 2012 ident: 10.1016/j.neunet.2014.03.010_br000095 article-title: Control of upper limb prostheses: terminology and proportional myoelectric control—a review publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering doi: 10.1109/TNSRE.2012.2196711 – volume: 21 start-page: 143 issue: 4 year: 2002 ident: 10.1016/j.neunet.2014.03.010_br000020 article-title: Hidden Markov model classification of myoelectric signals in speech publication-title: IEEE Engineering in Medicine and Biology Magazine doi: 10.1109/MEMB.2002.1044184 – ident: 10.1016/j.neunet.2014.03.010_br000260 doi: 10.1145/1357054.1357138 – volume: 44 start-page: 865 issue: 10 year: 2007 ident: 10.1016/j.neunet.2014.03.010_br000315 article-title: Classification of surface EMG signals using optimal wavelet packet method based on Davies–Bouldin criterion publication-title: Medical and Biological Engineering and Computing doi: 10.1007/s11517-006-0100-y – volume: 36 start-page: 830 issue: 4 year: 1990 ident: 10.1016/j.neunet.2014.03.010_br000320 article-title: Cross spectral analysis of non-stationary processes publication-title: IEEE Transactions on Information Theory doi: 10.1109/18.53742 – year: 1998 ident: 10.1016/j.neunet.2014.03.010_br000170 – volume: 162 start-page: 261 issue: 3 year: 1998 ident: 10.1016/j.neunet.2014.03.010_br000255 article-title: Energy supply and muscle fatigue in humans publication-title: Acta Physiologica Scandinavica doi: 10.1046/j.1365-201X.1998.0298f.x – volume: 54 start-page: 694 issue: 4 year: 2007 ident: 10.1016/j.neunet.2014.03.010_br000280 article-title: Myoelectric signal classification for phoneme-based speech recognition publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2006.889175 – year: 2009 ident: 10.1016/j.neunet.2014.03.010_br000295 – volume: 13 start-page: 1035 issue: 5 year: 2005 ident: 10.1016/j.neunet.2014.03.010_br000010 article-title: A tutorial on onset detection in music signals publication-title: IEEE Transactions on Speech and Audio Processing doi: 10.1109/TSA.2005.851998 – ident: 10.1016/j.neunet.2014.03.010_br000105 – ident: 10.1016/j.neunet.2014.03.010_br000210 – year: 1985 ident: 10.1016/j.neunet.2014.03.010_br000005 – volume: 24 start-page: 86 issue: 2 year: 2012 ident: 10.1016/j.neunet.2014.03.010_br000040 article-title: The effects of weight and inertia of the prosthesis on the sensitivity of electromyographic pattern recognition in relax state publication-title: Journal of Prosthetics and Orthotics doi: 10.1097/JPO.0b013e3182524cce – volume: 12 start-page: 775 issue: 5 year: 1996 ident: 10.1016/j.neunet.2014.03.010_br000080 article-title: Myoelectric teleoperation of a complex robotic hand publication-title: IEEE Transactions on Robotics and Automation doi: 10.1109/70.538982 – volume: 40 start-page: 82 issue: 1 year: 1993 ident: 10.1016/j.neunet.2014.03.010_br000155 article-title: A new strategy for multifunction myoelectric control publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/10.204774 – volume: 15 start-page: 111 year: 2007 ident: 10.1016/j.neunet.2014.03.010_br000075 article-title: The optimal controller delay for myoelectric prostheses publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering doi: 10.1109/TNSRE.2007.891391 – year: 2009 ident: 10.1016/j.neunet.2014.03.010_br000085 – volume: BME-33 start-page: 1173 issue: 12 year: 1986 ident: 10.1016/j.neunet.2014.03.010_br000120 article-title: Short time Fourier analysis of the electromyogram: fast movements and constant contraction publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.1986.325697 – volume: 2005 start-page: 3165 issue: 19 year: 2005 ident: 10.1016/j.neunet.2014.03.010_br000200 article-title: Estimating alertness level based on EEG spectrum analysis publication-title: EURASIP Journal of Applied Signal Processing doi: 10.1155/ASP.2005.3165 – volume: 1 start-page: 2151 issue: 1 year: 2009 ident: 10.1016/j.neunet.2014.03.010_br000240 article-title: A novel feature extraction for robust EMG pattern recognition publication-title: Journal of Computing – ident: 10.1016/j.neunet.2014.03.010_br000310 – volume: 19 start-page: 644 issue: 6 year: 2011 ident: 10.1016/j.neunet.2014.03.010_br000090 article-title: Resolving the limb position effect in myoelectric pattern recognition publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering doi: 10.1109/TNSRE.2011.2163529 – volume: 52 start-page: 1801 issue: 11 year: 2005 ident: 10.1016/j.neunet.2014.03.010_br000140 article-title: A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2005.856295 – ident: 10.1016/j.neunet.2014.03.010_br000185 doi: 10.1109/ISSNIP.2011.6146521 – start-page: 99 year: 2011 ident: 10.1016/j.neunet.2014.03.010_br000195 article-title: Electromyography pattern-recognition-based control of powered multifunctional upper-limb prostheses – ident: 10.1016/j.neunet.2014.03.010_br000045 – volume: 47 start-page: 228 issue: 2 year: 2000 ident: 10.1016/j.neunet.2014.03.010_br000165 article-title: Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/10.821766 – volume: 4 start-page: 17 issue: 1 year: 1982 ident: 10.1016/j.neunet.2014.03.010_br000115 article-title: Multifunctional prosthesis and orthosis control via microcomputer identification of temporal pattern differences in single-site myoelectric signals publication-title: Journal of Biomedical Engineering doi: 10.1016/0141-5425(82)90021-8 – ident: 10.1016/j.neunet.2014.03.010_br000215 – ident: 10.1016/j.neunet.2014.03.010_br000055 – volume: 52 start-page: 2726 issue: 12 year: 2005 ident: 10.1016/j.neunet.2014.03.010_br000205 article-title: EEG-based drowsiness estimation for safety driving using independent component analysis publication-title: IEEE Transactions on Circuits and Systems- I, Reg. Papers doi: 10.1109/TCSI.2005.857555 – volume: 29 start-page: 148 issue: 5 year: 2012 ident: 10.1016/j.neunet.2014.03.010_br000160 article-title: Myoelectric control of artificial limbs—is there a need to change focus? publication-title: IEEE Signal Processing Magazine – volume: 39 start-page: 10731 year: 2012 ident: 10.1016/j.neunet.2014.03.010_br000190 article-title: Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2012.02.192 – volume: 48 start-page: 302 issue: 3 year: 2001 ident: 10.1016/j.neunet.2014.03.010_br000070 article-title: A wavelet-based continuous classification scheme for multifunction myoelectric control publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/10.914793 – volume: 58 start-page: 1698 issue: 6 year: 2011 ident: 10.1016/j.neunet.2014.03.010_br000270 article-title: Selective classification for improved robustness of myoelectric control under nonideal conditions publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2011.2113182 – volume: 24 start-page: 309 year: 2003 ident: 10.1016/j.neunet.2014.03.010_br000015 article-title: Evaluation of the forearm EMG signal features for the control of a prosthetic hand publication-title: Physiological Measurement doi: 10.1088/0967-3334/24/2/307 – volume: 57 start-page: 1410 issue: 6 year: 2010 ident: 10.1016/j.neunet.2014.03.010_br000175 article-title: Orthogonal fuzzy neighborhood discriminant analysis for multifunction myoelectric hand control publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2009.2039480 – ident: 10.1016/j.neunet.2014.03.010_br000180 doi: 10.1109/ISCIT.2007.4392044 – year: 2004 ident: 10.1016/j.neunet.2014.03.010_br000220 article-title: Electromyography physiology, engineering, and noninvasive applications – ident: 10.1016/j.neunet.2014.03.010_br000025 doi: 10.1109/CISP.2011.6100025 – volume: 3 start-page: 175 issue: 2 year: 2008 ident: 10.1016/j.neunet.2014.03.010_br000125 article-title: A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2007.11.005 – volume: 29 start-page: 306 issue: 3 year: 1970 ident: 10.1016/j.neunet.2014.03.010_br000135 article-title: EEG analysis based on time domain properties publication-title: Electroencephalography and Clinical Neurophysiology doi: 10.1016/0013-4694(70)90143-4 – volume: 56 start-page: 65 issue: 1 year: 2009 ident: 10.1016/j.neunet.2014.03.010_br000145 article-title: A strategy for identifying locomotion modes using surface electromyography publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2008.2003293 – ident: 10.1016/j.neunet.2014.03.010_br000235 – volume: 59 start-page: 1649 issue: 6 year: 2012 ident: 10.1016/j.neunet.2014.03.010_br000335 article-title: High-density myoelectric pattern recognition toward improved stroke rehabilitation publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2012.2191551 – volume: IROS start-page: 3511 year: 2005 ident: 10.1016/j.neunet.2014.03.010_br000030 article-title: Control of multifunction myoelectric hand using a real-time EMG pattern recognition publication-title: EEE/RSJ International Conference on Intelligent Robots and Systems – volume: 18 start-page: 49 issue: 1 year: 2010 ident: 10.1016/j.neunet.2014.03.010_br000130 article-title: Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering doi: 10.1109/TNSRE.2009.2039590 – volume: 38 start-page: 4058 issue: 4 year: 2011 ident: 10.1016/j.neunet.2014.03.010_br000250 article-title: Feature extraction of forearm EMG signals for prosthetics publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2010.09.068 – ident: 10.1016/j.neunet.2014.03.010_br000100 doi: 10.1109/BHI.2012.6211702 – volume: 42 start-page: 513 issue: 2 year: 2012 ident: 10.1016/j.neunet.2014.03.010_br000150 article-title: Extreme learning machine for regression and multiclass classification publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics doi: 10.1109/TSMCB.2011.2168604 – volume: 3 start-page: 324 issue: 4 year: 1995 ident: 10.1016/j.neunet.2014.03.010_br000330 article-title: EMG feature evaluation for movement control of upper extremity prostheses publication-title: IEEE Transactions on Rehabilitation Engineering doi: 10.1109/86.481972 – volume: 50 start-page: 848 issue: 7 year: 2003 ident: 10.1016/j.neunet.2014.03.010_br000065 article-title: A robust, real-time control scheme for multifunction myoelectric control publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2003.813539 – volume: 55 issue: 8 year: 2008 ident: 10.1016/j.neunet.2014.03.010_br000230 article-title: Support vector machine-based classification scheme for myoelectric control applied to upper limb publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2008.919734 – ident: 10.1016/j.neunet.2014.03.010_br000050 – volume: 7 issue: 21 year: 2010 ident: 10.1016/j.neunet.2014.03.010_br000300 article-title: Study of stability of time-domain features for electromyographic pattern recognition publication-title: Journal of NeuroEngineering and Rehabilitation – year: 2007 ident: 10.1016/j.neunet.2014.03.010_br000290 – volume: 41 start-page: 119 issue: 2 year: 1979 ident: 10.1016/j.neunet.2014.03.010_br000305 article-title: Factors influencing quantified surface EMGs publication-title: European Journal of Applied Physiology and Occupational Physiology doi: 10.1007/BF00421659 – volume: 76 start-page: 197 issue: 3 year: 1990 ident: 10.1016/j.neunet.2014.03.010_br000110 article-title: Changes in EEG mean frequency and spectral purity during spontaneous alpha blocking publication-title: Electroencephalography and Clinical Neurophysiology doi: 10.1016/0013-4694(90)90015-C – ident: 10.1016/j.neunet.2014.03.010_br000035 doi: 10.1109/IEMBS.2006.4397932 – ident: 10.1016/j.neunet.2014.03.010_br000265 doi: 10.1145/1753326.1753451 – ident: 10.1016/j.neunet.2014.03.010_br000060 – volume: 55 start-page: 1128 issue: 3 year: 2008 ident: 10.1016/j.neunet.2014.03.010_br000285 article-title: Online electromyographic control of a robotic prosthesis publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2007.909536 – volume: 39 start-page: 7420 issue: 8 year: 2012 ident: 10.1016/j.neunet.2014.03.010_br000245 article-title: Feature reduction and selection for EMG signal classification publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2012.01.102 |
| SSID | ssj0006843 |
| Score | 2.5232747 |
| Snippet | Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control... |
| SourceID | proquest pubmed pascalfrancis crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 42 |
| SubjectTerms | Adult Applied sciences Artificial Limbs Biological and medical sciences Computer science; control theory; systems Data processing. List processing. Character string processing Electrodiagnosis. Electric activity recording Electromyogram (EMG) Electromyography - methods Exact sciences and technology Feature extraction Female Humans Invariants Investigative techniques, diagnostic techniques (general aspects) Limbs Male Medical sciences Memory organisation. Data processing Movement - physiology Movements Myoelectric control Pattern recognition Pattern Recognition, Automated - methods Posture - physiology Segments Signal processing Signal Processing, Computer-Assisted Software Spectra Spectral moments Time Factors User-Computer Interface Young Adult |
| Title | Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features |
| URI | https://dx.doi.org/10.1016/j.neunet.2014.03.010 https://www.ncbi.nlm.nih.gov/pubmed/24721224 https://www.proquest.com/docview/1530951806 https://www.proquest.com/docview/1540224464 https://www.proquest.com/docview/1671607353 |
| Volume | 55 |
| WOSCitedRecordID | wos000337860600005&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-2782 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006843 issn: 0893-6080 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db5swELfSdg-Tpn13yz4iT5r2gpgIGAyP1ZRqq9psD2mVN8sYW6FLSJaEqNtfvzPGkKjqxx72giIwmPh-nO_su98h9FEG4CSHKnazgFCXKBG7cZwJl_oZ5UoImVZBNBendDiMx-PkR6fzx-bCbKa0KOKrq2TxX0UN50DYOnX2H8TdPBROwG8QOhxB7HC8n-CrQNiVM81nqWNjspy82IBXDMPozH7PTe2bXGhWVb0g6DRhRNCyrFYPdM1511bIXTtVQqbO5VeyYgJdbRu1muADLhUmorzdHZqUqwlPjXXKZ3m76zPiP5elSXE_420u2llukm5OOHTuXPDptH3WPOPgQXO7iL2ebC9X9EkT2tpotSRwI8-Ub7IqOAy3dKhh26pnY8Prfk3PmyWHy8-FLOHP6Qg9UnHV1iGyO7Taw-_s-Pz0lI0G49GnxS9XVxzTO_N1-ZU9dODTMAGNeHD0bTA-aebxKDYxl_aFbeJlFR14veObDJtHC76Cz02ZOik3OzKVQTN6ih7Xngg-Mgh6hjqyeI6e2CofuFb6L5CqAYU1oLAFFG4AhbcAhWtA4S1A4QpQeBdQ2AIKW0C9ROfHg9GXr25dncMVoU_WMCRCKhmlMAF4Qag86nsq8jX5UBb1o0T6isQiVQJO0ShMFe9LxZXiWZCBdkiy4BDtF_NCvkY4DbkM0oykpO8RoaQmuYTZgcNzQcFkSRcFdmiZqKnrdQWVKbMxipfMCIRpgTAvYCCQLnKbuxaGuuWO9tRKjdXmpzErGaDujjt7O0JuuvPBn9EMd130wUqdgfrWe3K8kPNyxcDg0E5O7N3ahmhLm0TkljYR1VSRQRh00SsDq_YtCPX1Bvqbe_TwFj1sv9p3aH-9LOV79EBs1vlq2UN7dBz36g_lL0QB5iE |
| linkProvider | Elsevier |
| 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=Towards+limb+position+invariant+myoelectric+pattern+recognition+using+time-dependent+spectral+features&rft.jtitle=Neural+networks&rft.au=Khushaba%2C+Rami+N&rft.au=Takruri%2C+Maen&rft.au=Miro%2C+Jaime+Valls&rft.au=Kodagoda%2C+Sarath&rft.date=2014-07-01&rft.issn=0893-6080&rft.volume=55&rft.spage=42&rft.epage=58&rft_id=info:doi/10.1016%2Fj.neunet.2014.03.010&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0893-6080&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0893-6080&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0893-6080&client=summon |