{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T05:17:04Z","timestamp":1788499024358,"version":"build-2803163510"},"reference-count":35,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T00:00:00Z","timestamp":1787961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011789","name":"Jilin Province Science and Technology Department","doi-asserted-by":"crossref","award":["YDZJ202501ZYTS629"],"award-info":[{"award-number":["YDZJ202501ZYTS629"]}],"id":[{"id":"10.13039\/501100011789","id-type":"DOI","asserted-by":"crossref"}]},{"award":["YDZJ202501ZYTS629"],"award-info":[{"award-number":["YDZJ202501ZYTS629"]}],"id":[{"id":"https:\/\/ror.org\/049x38272","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Zhongshan Innovative Research Team Project","award":["CXTD2023007"],"award-info":[{"award-number":["CXTD2023007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface electromyography (sEMG)-based gesture recognition has attracted considerable attention in intelligent prosthesis control, human\u2013computer interaction, and rehabilitation assistance. However, practical deployment remains challenging because new users can usually provide only a few labeled samples for calibration. Under cross-subject and posture-varying conditions, this setting further aggravates distribution shifts and can destabilize target-domain adaptation. To address these issues, this paper proposes a cross-subject sEMG gesture-recognition framework integrating training-time augmentation, two-stage meta-transfer learning, and matched feature regularization. Model-agnostic meta-learning (MAML) is first used on source-subject data to learn an initialization for rapid transfer, after which target-subject fine-tuning is performed with an auxiliary class-consistent feature constraint. Experiments were conducted on a self-collected 11-subject multi-posture dataset using a leave-one-subject-out (LOSO) protocol under FT-1_full, which uses one complete calibration repetition, and FT-2_k10, which uses 10 windows per gesture per posture. The proposed MAML + FT + MATCHED method achieved higher average Accuracy and Macro-F1 than Source-pretrain + FT. Under FT-2_k10, the average improvements were 9.20 and 9.70 percentage points, respectively. Nevertheless, the primary subject-level paired Wilcoxon comparisons did not reach statistical significance (Accuracy: p = 0.2402; Macro-F1: p = 0.2402; Holm-adjusted p = 1.0000 for both). The results therefore indicate favorable average trends and positive effect sizes, while pairwise statistical superiority was not established in the present 11-subject cohort.<\/jats:p>","DOI":"10.3390\/s26175476","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:55:56Z","timestamp":1788155756000},"page":"5476","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Two-Stage Meta-Learning with Matched Feature Regularization for Cross-Subject sEMG Gesture Recognition Under Posture Variation"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2716-449X","authenticated-orcid":false,"given":"Qi","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Changchun University of Science and Technology, No. 7186 Weixing Road, Changchun 130022, China"},{"name":"Zhongshan Institute, Changchun University of Science and Technology, Zhongshan 528437, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"He","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Changchun University of Science and Technology, No. 7186 Weixing Road, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Changchun University of Science and Technology, No. 7186 Weixing Road, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114517","DOI":"10.1016\/j.isci.2025.114517","article-title":"Advances in electromyography armbands for gesture recognition and multimodal fusion","volume":"29","author":"Zhang","year":"2026","journal-title":"iScience"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3015","DOI":"10.1109\/TNSRE.2023.3295453","article-title":"Transfer learning on electromyography (EMG) tasks: Approaches and beyond","volume":"31","author":"Wu","year":"2023","journal-title":"IEEE Trans. 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