An extended variational autoencoder for cross-subject electromyograph gesture recognition
•The study proposes a novel cross-subject gesture recognition approach.•An extended VAE is designed to disentangle input data into three representations.•A competitive voting strategy is to further bolster accuracy and stability in recognition.•The performance of the proposed method is evaluated on...
Uloženo v:
| Vydáno v: | Biomedical signal processing and control Ročník 99; s. 106828 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.01.2025
|
| Témata: | |
| ISSN: | 1746-8094 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | •The study proposes a novel cross-subject gesture recognition approach.•An extended VAE is designed to disentangle input data into three representations.•A competitive voting strategy is to further bolster accuracy and stability in recognition.•The performance of the proposed method is evaluated on the Myo dataset.•The source code and the Myo dataset will be publicly available.
Surface electromyographic hand gesture recognition has gained significant attention in recent years, especially within the field of human–computer interfaces. However, cross-subject tasks remain challenging due to inherent individual differences. To address this, a novel approach for hand gesture recognition is proposed that leverages a subject-generalized variational autoencoder. This approach involves an extended variational autoencoder designed to disentangle input data into three distinct feature-specific representations. The primary classifier within the variational autoencoder focuses on gesture recognition, while two auxiliary classifiers work together to extract subject-specific and gesture-specific features. The gesture-specific features capture generalized characteristics applicable across all subjects, enabling direct application to new subjects. To enhance accuracy and stability, a competitive voting strategy is implemented. The effectiveness of the proposed method was evaluated using a dataset comprising six representative gestures performed by eight subjects. Comparative analysis with baseline models shows that our approach outperforms others, demonstrating superior generalization with an average accuracy of 90.52% in cross-subject validation. |
|---|---|
| AbstractList | •The study proposes a novel cross-subject gesture recognition approach.•An extended VAE is designed to disentangle input data into three representations.•A competitive voting strategy is to further bolster accuracy and stability in recognition.•The performance of the proposed method is evaluated on the Myo dataset.•The source code and the Myo dataset will be publicly available.
Surface electromyographic hand gesture recognition has gained significant attention in recent years, especially within the field of human–computer interfaces. However, cross-subject tasks remain challenging due to inherent individual differences. To address this, a novel approach for hand gesture recognition is proposed that leverages a subject-generalized variational autoencoder. This approach involves an extended variational autoencoder designed to disentangle input data into three distinct feature-specific representations. The primary classifier within the variational autoencoder focuses on gesture recognition, while two auxiliary classifiers work together to extract subject-specific and gesture-specific features. The gesture-specific features capture generalized characteristics applicable across all subjects, enabling direct application to new subjects. To enhance accuracy and stability, a competitive voting strategy is implemented. The effectiveness of the proposed method was evaluated using a dataset comprising six representative gestures performed by eight subjects. Comparative analysis with baseline models shows that our approach outperforms others, demonstrating superior generalization with an average accuracy of 90.52% in cross-subject validation. |
| ArticleNumber | 106828 |
| Author | Shen, Quming Zhang, Yuhui Ming, Yuewei Wang, Yanyu Zhang, Zhen |
| Author_xml | – sequence: 1 givenname: Zhen orcidid: 0000-0001-6966-0208 surname: Zhang fullname: Zhang, Zhen email: zhangzhen_ta@shu.edu.cn organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 2 givenname: Yuewei surname: Ming fullname: Ming, Yuewei organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 3 givenname: Quming surname: Shen fullname: Shen, Quming organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 4 givenname: Yanyu surname: Wang fullname: Wang, Yanyu organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 5 givenname: Yuhui surname: Zhang fullname: Zhang, Yuhui organization: Department of Spine Surgery, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200127, China |
| BookMark | eNp9kD1rwzAQhjWk0CTtH-ikP-BUsl1Zhi4h9AsCXdqhk9DHKZVxpCApofn3tZNOHbLcwR3Pce8zQxMfPCB0R8mCEsruu4VKO70oSVkPA8ZLPkFT2tSs4KStr9EspY6Qmje0nqKvpcfwk8EbMPggo5PZBS97LPc5gNfBQMQ2RKxjSKlIe9WBzhj6ocawPYZNlLtvvIGU9xFwBB023o03btCVlX2C278-R5_PTx-r12L9_vK2Wq4LXRGSCyVpBQQeNGUNV7bSDWOgFVOWGdOoujRcDXvbtKqkpG1bKi1QTgzTVW2IrOaoPN89fRjBil10WxmPghIxChGdGIWIUYg4Cxkg_g_SLp-i5yhdfxl9PKMwhDo4iCJpN5gC44b0WZjgLuG_kJ-Djw |
| CitedBy_id | crossref_primary_10_3390_s25134119 |
| Cites_doi | 10.1016/j.bspc.2023.105935 10.1109/TAI.2021.3098253 10.1016/j.engappai.2024.108952 10.1142/S0218001421510125 10.1109/TNSRE.2023.3293334 10.3390/s19143170 10.1109/TNSRE.2022.3173946 10.3390/bioengineering10091101 10.1109/JBHI.2020.3009383 10.1109/TNSRE.2015.2420654 10.3390/s20041113 10.3390/s17030458 10.1109/TNSRE.2019.2896269 10.3390/s20092467 10.1115/1.4056325 10.3390/s20143994 10.18494/SAM.2020.2652 10.1016/j.rser.2022.112473 10.1016/j.bspc.2023.104613 10.1088/1741-2552/ad184f 10.1016/j.engappai.2023.107251 10.1109/EMBC40787.2023.10340691 10.1109/ETCM.2017.8247458 10.1016/j.neucom.2021.12.081 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier Ltd |
| Copyright_xml | – notice: 2024 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.bspc.2024.106828 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| ExternalDocumentID | 10_1016_j_bspc_2024_106828 S1746809424008863 |
| GroupedDBID | --- --K --M .~1 0R~ 1B1 1~. 1~5 23N 4.4 457 4G. 5GY 5VS 6J9 7-5 71M 8P~ AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAXKI AAXUO AAYFN ABBOA ABFNM ABFRF ABJNI ABMAC ABXDB ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFJKZ AFKWA AFTJW AGHFR AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HZ~ IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SES SPC SPCBC SST SSV SSZ T5K UNMZH ~G- 9DU AATTM AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFPUW AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c300t-ba13e0e5c1678bf3c766ecb6bf6dd7b42d8be0ef79b2109991afe180d6c34d0a3 |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001315504100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1746-8094 |
| IngestDate | Sat Nov 29 02:51:28 EST 2025 Tue Nov 18 22:18:28 EST 2025 Sat Nov 09 16:00:03 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Variational autoencoder Competitive voting Surface electromyographic Cross-subject Feature disentanglement Gesture recognition |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-ba13e0e5c1678bf3c766ecb6bf6dd7b42d8be0ef79b2109991afe180d6c34d0a3 |
| ORCID | 0000-0001-6966-0208 |
| ParticipantIDs | crossref_primary_10_1016_j_bspc_2024_106828 crossref_citationtrail_10_1016_j_bspc_2024_106828 elsevier_sciencedirect_doi_10_1016_j_bspc_2024_106828 |
| PublicationCentury | 2000 |
| PublicationDate | January 2025 2025-01-00 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – month: 01 year: 2025 text: January 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | Biomedical signal processing and control |
| PublicationYear | 2025 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Zhang, Yang, Qian, Zhang (b0030) 2019; 19 Ilse M, Tomczak J M, Louizos C, Welling M. Diva: Domain invariant variational autoencoders. Medical Imaging with Deep Learning. PMLR, 2020: 322-348. Hoshino, Kanoga, Tsubaki, Aoyama (b0095) 2022; 489 Côté-Allard, Fall, Drouin, Campeau-Lecours, Gosselin, Glette, Laviolette, Gosselin (b0115) 2019; 27 Du, Jin, Wei, Hu, Geng (b0110) 2017; 17 D.P. Kingma, Welling M. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. Liu, Peng, Tan, Oyemakinde, Wang, Li, Li (b0145) 2024; 20 Zhang, Shen, Wang (b0060) 2024; 91 Benalcazar M E, Motoche C, Zea J A, Jaramillo A G, Anchundia C E, Zambrano P, Segura M, Benalcazar P, Perez M. Real-time hand gesture recognition using the Myo armband and muscle activity detection. 2017 IEEE 2nd Ecuador Tech. Chapters Meet. ETCM 2017 2018, 2017-Janua, 1-6. Ye Y, He Y, Pan T, Dong Q, Yuan J, Zhou W. Cross-subject EMG hand gesture recognition based on dynamic domain generalization. 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, 2023, 1-4. Tang, Yang, Zhang, Zhang (b0075) 2022; 162 Dai, Wong, Kankanhali, Li, Geng (b0140) 2023; 10 Su, Liu, Qian, Zhang, Zhang (b0055) 2021; 35 Wang, Chen, Zhang, Yang (b0130) 2023; 31 Zhang, Liu, Wang, Song, Zhang (b0020) 2024; 127 Xu, Shen, Qian, Zhang (b0045) 2020; 20 Wang, Chen, Zhang, Yang, Hu (b0085) 2023; 31 Wang, Zhao, Zhang (b0005) 2023; 121055 Guerrero-López A, Sevilla-Salcedo C, Gómez-Verdejo V, Olmos P M. Multi-view hierarchical Variational AutoEncoders with Factor Analysis latent space. arXiv preprint arXiv:2207.09185, 2022. Tang, Kuo, Zhang (b0080) 2023 Zhang, Ming, Wang (bib166) 2024; 136 I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, A. Lerchner, beta-vae: Learning basic visual concepts with a constrained variational framework, in: International conference on learning representations, 2016. Zhang, Zhang, Wu, Li, Chen, Chen (b0100) 2022; 30 Zou, Cheng (b0105) 2021; 2 Hua, Wang, Lam, Wen (b0025) 2023; 83 Wang, Lan, Liu, Ouyang, Qin, Lu, Chen, Zeng, Yu (b0120) 2023; 35 Zhang, He, Yang (b0050) 2020; 20 Ge, Wu, Han, Zhao (b0010) 2023; 6 Jaramillo-Yánez, Benalcázar, Mena-Maldonado (b0015) 2020; 20 Liu, Sheng, Zhang, Jiang, Zhu (b0065) 2015; 24 Fan, Jiang, Lin, Li, Fiaidhi, Ma, Wu (b0090) 2021 Zhang, Yu, Qian (b0040) 2020; 32 Chen, Li, Hu, Zhang, Chen (b0070) 2020; 25 Fan, Jiang, Liu, Meng, Jia, Dai (b0135) 2024; 2024 Zhang (10.1016/j.bspc.2024.106828_b0060) 2024; 91 Ge (10.1016/j.bspc.2024.106828_b0010) 2023; 6 Tang (10.1016/j.bspc.2024.106828_b0080) 2023 Zhang (10.1016/j.bspc.2024.106828_bib166) 2024; 136 Zou (10.1016/j.bspc.2024.106828_b0105) 2021; 2 Wang (10.1016/j.bspc.2024.106828_b0130) 2023; 31 10.1016/j.bspc.2024.106828_b0125 Du (10.1016/j.bspc.2024.106828_b0110) 2017; 17 Liu (10.1016/j.bspc.2024.106828_b0145) 2024; 20 Hua (10.1016/j.bspc.2024.106828_b0025) 2023; 83 Chen (10.1016/j.bspc.2024.106828_b0070) 2020; 25 10.1016/j.bspc.2024.106828_b0165 Zhang (10.1016/j.bspc.2024.106828_b0020) 2024; 127 Su (10.1016/j.bspc.2024.106828_b0055) 2021; 35 Liu (10.1016/j.bspc.2024.106828_b0065) 2015; 24 Jaramillo-Yánez (10.1016/j.bspc.2024.106828_b0015) 2020; 20 Wang (10.1016/j.bspc.2024.106828_b0005) 2023; 121055 Hoshino (10.1016/j.bspc.2024.106828_b0095) 2022; 489 10.1016/j.bspc.2024.106828_b0160 Zhang (10.1016/j.bspc.2024.106828_b0100) 2022; 30 Côté-Allard (10.1016/j.bspc.2024.106828_b0115) 2019; 27 Wang (10.1016/j.bspc.2024.106828_b0120) 2023; 35 Zhang (10.1016/j.bspc.2024.106828_b0040) 2020; 32 Zhang (10.1016/j.bspc.2024.106828_b0030) 2019; 19 Wang (10.1016/j.bspc.2024.106828_b0085) 2023; 31 10.1016/j.bspc.2024.106828_b0035 10.1016/j.bspc.2024.106828_b0155 Dai (10.1016/j.bspc.2024.106828_b0140) 2023; 10 Zhang (10.1016/j.bspc.2024.106828_b0050) 2020; 20 10.1016/j.bspc.2024.106828_b0150 Fan (10.1016/j.bspc.2024.106828_b0135) 2024; 2024 Tang (10.1016/j.bspc.2024.106828_b0075) 2022; 162 Xu (10.1016/j.bspc.2024.106828_b0045) 2020; 20 Fan (10.1016/j.bspc.2024.106828_b0090) 2021 |
| References_xml | – reference: D.P. Kingma, Welling M. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. – volume: 19 start-page: 3170 year: 2019 ident: b0030 article-title: Real-time surface EMG pattern recognition for hand gestures based on an artificial neural network publication-title: Sensors – volume: 121055 year: 2023 ident: b0005 article-title: A deep learning approach using attention mechanism and transfer learning for electromyographic hand gesture estimation publication-title: Expert Syst. Appl. – volume: 6 year: 2023 ident: b0010 article-title: Gesture recognition and master–slave control of a manipulator based on sEMG and convolutional neural network–gated recurrent unit publication-title: Journal of Engineering and Science in Medical Diagnostics and Therapy – reference: Benalcazar M E, Motoche C, Zea J A, Jaramillo A G, Anchundia C E, Zambrano P, Segura M, Benalcazar P, Perez M. Real-time hand gesture recognition using the Myo armband and muscle activity detection. 2017 IEEE 2nd Ecuador Tech. Chapters Meet. ETCM 2017 2018, 2017-Janua, 1-6. – start-page: 127864 year: 2023 ident: b0080 article-title: Zhang Z – volume: 20 start-page: 3994 year: 2020 ident: b0050 article-title: A novel surface electromyographic signal-based hand gesture prediction using a recurrent neural network publication-title: Sensors – reference: Ye Y, He Y, Pan T, Dong Q, Yuan J, Zhou W. Cross-subject EMG hand gesture recognition based on dynamic domain generalization. 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, 2023, 1-4. – volume: 31 start-page: 2974 year: 2023 end-page: 2987 ident: b0130 article-title: Iterative self-training based domain adaptation for cross-user sEMG gesture recognition publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – reference: Guerrero-López A, Sevilla-Salcedo C, Gómez-Verdejo V, Olmos P M. Multi-view hierarchical Variational AutoEncoders with Factor Analysis latent space. arXiv preprint arXiv:2207.09185, 2022. – volume: 10 start-page: 1101 year: 2023 ident: b0140 article-title: Improved network and training scheme for cross-trial surface rlectromyography (sEMG)-based gesture recognition publication-title: Bioengineering – volume: 24 start-page: 444 year: 2015 end-page: 454 ident: b0065 article-title: Towards zero retraining for myoelectric control based on common model component analysis publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 27 start-page: 760 year: 2019 end-page: 771 ident: b0115 article-title: Deep learning for electromyographic hand gesture signal classification using transfer learning publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 35 start-page: 2151012 year: 2021 ident: b0055 article-title: Hand gesture recognition based on sEMG signal and convolutional neural network publication-title: Int. J. Pattern Recognit Artif Intell. – volume: 35 start-page: 8052 year: 2023 end-page: 8072 ident: b0120 article-title: Generalizing to unseen domains: A survey on domain generalization publication-title: IEEE Trans. Knowl. Data Eng. – volume: 83 year: 2023 ident: b0025 article-title: An incremental learning method with hybrid data over/down-sampling for sEMG-based gesture classification publication-title: Biomed. Signal Process. Control – volume: 162 year: 2022 ident: b0075 article-title: Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy publication-title: Renew. Sustain. Energy Rev. – volume: 136 year: 2024 ident: bib166 article-title: A federated transfer learning approach for surface electromyographic hand gesture recognition with emphasis on privacy preservation publication-title: Engineering Applications of Artificial Intelligence – volume: 489 start-page: 599 year: 2022 end-page: 612 ident: b0095 article-title: Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers publication-title: Neurocomputing – volume: 32 start-page: 1523 year: 2020 end-page: 1532 ident: b0040 article-title: Classification of finger movements for prosthesis control with surface electromyography publication-title: Sensors and Materials – volume: 2 start-page: 447 year: 2021 end-page: 458 ident: b0105 article-title: A transfer learning model for gesture recognition based on the deep features extracted by CNN publication-title: IEEE Transactions on Artificial Intelligence – volume: 17 start-page: 458 year: 2017 ident: b0110 article-title: Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation publication-title: Sensors – volume: 91 year: 2024 ident: b0060 article-title: Electromyographic hand gesture recognition using convolutional neural network with multi-attention publication-title: Biomed. Signal Process. Control – volume: 31 start-page: 2974 year: 2023 end-page: 2987 ident: b0085 article-title: Iterative Self-Training based Domain Adaptation for Cross-User sEMG Gesture Recognition publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 20 start-page: 1113 year: 2020 ident: b0045 article-title: Advanced hand gesture prediction robust to electrode shift with an arbitrary angle publication-title: Sensors – start-page: 1 year: 2021 end-page: 11 ident: b0090 article-title: Improving sEMG-based motion intention recognition for upper-limb amputees using transfer learning publication-title: Neural Comput. & Applic. – volume: 20 year: 2024 ident: b0145 article-title: A novel unsupervised dynamic feature domain adaptation strategy for cross-individual myoelectric gesture recognition publication-title: J. Neural Eng. – volume: 25 start-page: 1292 year: 2020 end-page: 1304 ident: b0070 article-title: Hand gesture recognition based on surface electromyography using convolutional neural network with transfer learning method publication-title: IEEE J. Biomed. Health Inform. – reference: Ilse M, Tomczak J M, Louizos C, Welling M. Diva: Domain invariant variational autoencoders. Medical Imaging with Deep Learning. PMLR, 2020: 322-348. – volume: 2024 year: 2024 ident: b0135 article-title: Surface EMG feature disentanglement for robust pattern recognition publication-title: Expert Syst. Appl. – volume: 127 year: 2024 ident: b0020 article-title: Online electromyographic hand gesture recognition using deep learning and transfer learning publication-title: Eng. Appl. Artif. Intel. – volume: 30 start-page: 1374 year: 2022 end-page: 1383 ident: b0100 article-title: Domain adaptation with self-guided adaptive sampling strategy: Feature alignment for cross-user myoelectric pattern recognition publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 20 start-page: 2467 year: 2020 ident: b0015 article-title: Real-time hand gesture recognition using surface electromyography and machine learning: a systematic literature review publication-title: Sensors – reference: I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, A. Lerchner, beta-vae: Learning basic visual concepts with a constrained variational framework, in: International conference on learning representations, 2016. – volume: 91 year: 2024 ident: 10.1016/j.bspc.2024.106828_b0060 article-title: Electromyographic hand gesture recognition using convolutional neural network with multi-attention publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2023.105935 – volume: 2 start-page: 447 issue: 5 year: 2021 ident: 10.1016/j.bspc.2024.106828_b0105 article-title: A transfer learning model for gesture recognition based on the deep features extracted by CNN publication-title: IEEE Transactions on Artificial Intelligence doi: 10.1109/TAI.2021.3098253 – volume: 136 year: 2024 ident: 10.1016/j.bspc.2024.106828_bib166 article-title: A federated transfer learning approach for surface electromyographic hand gesture recognition with emphasis on privacy preservation publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2024.108952 – volume: 35 start-page: 2151012 issue: 11 year: 2021 ident: 10.1016/j.bspc.2024.106828_b0055 article-title: Hand gesture recognition based on sEMG signal and convolutional neural network publication-title: Int. J. Pattern Recognit Artif Intell. doi: 10.1142/S0218001421510125 – volume: 2024 issue: 237 year: 2024 ident: 10.1016/j.bspc.2024.106828_b0135 article-title: Surface EMG feature disentanglement for robust pattern recognition publication-title: Expert Syst. Appl. – volume: 31 start-page: 2974 year: 2023 ident: 10.1016/j.bspc.2024.106828_b0085 article-title: Iterative Self-Training based Domain Adaptation for Cross-User sEMG Gesture Recognition publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2023.3293334 – volume: 121055 year: 2023 ident: 10.1016/j.bspc.2024.106828_b0005 article-title: A deep learning approach using attention mechanism and transfer learning for electromyographic hand gesture estimation publication-title: Expert Syst. Appl. – volume: 19 start-page: 3170 issue: 14 year: 2019 ident: 10.1016/j.bspc.2024.106828_b0030 article-title: Real-time surface EMG pattern recognition for hand gestures based on an artificial neural network publication-title: Sensors doi: 10.3390/s19143170 – volume: 30 start-page: 1374 year: 2022 ident: 10.1016/j.bspc.2024.106828_b0100 article-title: Domain adaptation with self-guided adaptive sampling strategy: Feature alignment for cross-user myoelectric pattern recognition publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2022.3173946 – volume: 10 start-page: 1101 year: 2023 ident: 10.1016/j.bspc.2024.106828_b0140 article-title: Improved network and training scheme for cross-trial surface rlectromyography (sEMG)-based gesture recognition publication-title: Bioengineering doi: 10.3390/bioengineering10091101 – volume: 25 start-page: 1292 issue: 4 year: 2020 ident: 10.1016/j.bspc.2024.106828_b0070 article-title: Hand gesture recognition based on surface electromyography using convolutional neural network with transfer learning method publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2020.3009383 – volume: 24 start-page: 444 issue: 4 year: 2015 ident: 10.1016/j.bspc.2024.106828_b0065 article-title: Towards zero retraining for myoelectric control based on common model component analysis publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2015.2420654 – volume: 31 start-page: 2974 year: 2023 ident: 10.1016/j.bspc.2024.106828_b0130 article-title: Iterative self-training based domain adaptation for cross-user sEMG gesture recognition publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2023.3293334 – volume: 20 start-page: 1113 issue: 4 year: 2020 ident: 10.1016/j.bspc.2024.106828_b0045 article-title: Advanced hand gesture prediction robust to electrode shift with an arbitrary angle publication-title: Sensors doi: 10.3390/s20041113 – ident: 10.1016/j.bspc.2024.106828_b0165 – volume: 17 start-page: 458 issue: 3 year: 2017 ident: 10.1016/j.bspc.2024.106828_b0110 article-title: Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation publication-title: Sensors doi: 10.3390/s17030458 – start-page: 127864 year: 2023 ident: 10.1016/j.bspc.2024.106828_b0080 – volume: 27 start-page: 760 issue: 4 year: 2019 ident: 10.1016/j.bspc.2024.106828_b0115 article-title: Deep learning for electromyographic hand gesture signal classification using transfer learning publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2019.2896269 – volume: 20 start-page: 2467 issue: 9 year: 2020 ident: 10.1016/j.bspc.2024.106828_b0015 article-title: Real-time hand gesture recognition using surface electromyography and machine learning: a systematic literature review publication-title: Sensors doi: 10.3390/s20092467 – volume: 6 issue: 2 year: 2023 ident: 10.1016/j.bspc.2024.106828_b0010 article-title: Gesture recognition and master–slave control of a manipulator based on sEMG and convolutional neural network–gated recurrent unit publication-title: Journal of Engineering and Science in Medical Diagnostics and Therapy doi: 10.1115/1.4056325 – volume: 20 start-page: 3994 issue: 14 year: 2020 ident: 10.1016/j.bspc.2024.106828_b0050 article-title: A novel surface electromyographic signal-based hand gesture prediction using a recurrent neural network publication-title: Sensors doi: 10.3390/s20143994 – ident: 10.1016/j.bspc.2024.106828_b0150 – volume: 32 start-page: 1523 year: 2020 ident: 10.1016/j.bspc.2024.106828_b0040 article-title: Classification of finger movements for prosthesis control with surface electromyography publication-title: Sensors and Materials doi: 10.18494/SAM.2020.2652 – ident: 10.1016/j.bspc.2024.106828_b0155 – volume: 162 year: 2022 ident: 10.1016/j.bspc.2024.106828_b0075 article-title: Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2022.112473 – volume: 83 year: 2023 ident: 10.1016/j.bspc.2024.106828_b0025 article-title: An incremental learning method with hybrid data over/down-sampling for sEMG-based gesture classification publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2023.104613 – volume: 20 issue: 6 year: 2024 ident: 10.1016/j.bspc.2024.106828_b0145 article-title: A novel unsupervised dynamic feature domain adaptation strategy for cross-individual myoelectric gesture recognition publication-title: J. Neural Eng. doi: 10.1088/1741-2552/ad184f – volume: 127 year: 2024 ident: 10.1016/j.bspc.2024.106828_b0020 article-title: Online electromyographic hand gesture recognition using deep learning and transfer learning publication-title: Eng. Appl. Artif. Intel. doi: 10.1016/j.engappai.2023.107251 – ident: 10.1016/j.bspc.2024.106828_b0125 doi: 10.1109/EMBC40787.2023.10340691 – ident: 10.1016/j.bspc.2024.106828_b0160 – ident: 10.1016/j.bspc.2024.106828_b0035 doi: 10.1109/ETCM.2017.8247458 – volume: 489 start-page: 599 year: 2022 ident: 10.1016/j.bspc.2024.106828_b0095 article-title: Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.12.081 – volume: 35 start-page: 8052 issue: 8 year: 2023 ident: 10.1016/j.bspc.2024.106828_b0120 article-title: Generalizing to unseen domains: A survey on domain generalization publication-title: IEEE Trans. Knowl. Data Eng. – start-page: 1 year: 2021 ident: 10.1016/j.bspc.2024.106828_b0090 article-title: Improving sEMG-based motion intention recognition for upper-limb amputees using transfer learning publication-title: Neural Comput. & Applic. |
| SSID | ssj0048714 |
| Score | 2.3690746 |
| Snippet | •The study proposes a novel cross-subject gesture recognition approach.•An extended VAE is designed to disentangle input data into three representations.•A... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 106828 |
| SubjectTerms | Competitive voting Cross-subject Feature disentanglement Gesture recognition Surface electromyographic Variational autoencoder |
| Title | An extended variational autoencoder for cross-subject electromyograph gesture recognition |
| URI | https://dx.doi.org/10.1016/j.bspc.2024.106828 |
| Volume | 99 |
| WOSCitedRecordID | wos001315504100001&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 issn: 1746-8094 databaseCode: AIEXJ dateStart: 20060101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0048714 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT-MwELbK4wAHxGMRb_mwtyooTtLEOVYrECBAILFS4RLFr90iCBVtuuxlfzvj2E5DeWg5cImqNHGizKfxeDzfNwh9l4qTSKahB8ZVXhQE3KMh9T2fpYQqLngQV0Th0-T8nPZ66UWr9c9xYcZ3SVHQp6d08KWmhnNgbE2d_YS560HhBPwGo8MRzA7H_zJ8V8v2m8x2ewwrYZfty8vRg1at1OIRurawmh-9Ycl0JqZt2-Hc_zUS1m297aT3FuoCI2s-t_9bsfYNpbL_Sw8_MIwDx3m0JfCv8tI3vyfcszPbT-W6lH9kv871WL7IZXnvptUq4W-vBedVNlMVQaeRqjDeNYm0-rHpauzcr-mPZP0nLFCpIYu_cu0my3C7z4YDLT0ZRPuTi1_qaE_Nb3XVoStou830GJkeIzNjzKC5IOmk4BXnuscHvRM3l8NqrlKHr1_c0q5MheD0m7wd2jTClatltGTXGbhr8LGCWrJYRYsN9ck1dN0tsEMKbiAFN5CCASn4BVLwFFKwRQpuIOUb-nl4cPXjyLOdNjwe-v7IYzkJpS87nEDswlTIkziWnMVMxUIkLAoEZfC_SlIWkGpNkStJqC9iHkbCz8N1NFs8FHIDYS3mI2RKGOUqUirNOWcQE7JQ8JhIJjYRcR8p41aGXndDucveN88matf3DIwIy4dXd9y3z2wYacLDDKD0wX1bn3rKNlqYQHwHzY4eS7mL5vl41B8-7lkcPQNJHZon |
| 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=An+extended+variational+autoencoder+for+cross-subject+electromyograph+gesture+recognition&rft.jtitle=Biomedical+signal+processing+and+control&rft.au=Zhang%2C+Zhen&rft.au=Ming%2C+Yuewei&rft.au=Shen%2C+Quming&rft.au=Wang%2C+Yanyu&rft.date=2025-01-01&rft.issn=1746-8094&rft.volume=99&rft.spage=106828&rft_id=info:doi/10.1016%2Fj.bspc.2024.106828&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_bspc_2024_106828 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1746-8094&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1746-8094&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1746-8094&client=summon |