{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T04:31:27Z","timestamp":1786509087805,"version":"build-2736575974"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T00:00:00Z","timestamp":1786233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Electromyography (EMG)-based pattern recognition is a key enabling technology for intuitive upper-limb prosthetic control; however, amputee EMG signals show substantial intersubject variability and nonstationary characteristics, which can limit the effectiveness of conventional fixed feature representations. This study proposes an Adaptive Multi-Representation Fusion Framework that combines an enhanced Adaptive Linear Series Decomposition Learner (Adaptive LSDL) with Time-Domain Power Spectral Descriptors (TDPSD) for amputee movement classification. The proposed Adaptive LSDL extends conventional decomposition by using adaptive threshold-region optimization and multitransform signal analysis, enabling subject-specific identification of the most discriminative decomposition characteristics while preserving low computational complexity. The complementary properties of TDPSD and Adaptive LSDL are then exploited through feature-level fusion, producing a unified representation that captures both local temporal-spectral complexity and adaptive structural signal information. Experimental evaluation was conducted using the Ninapro DB3 dataset, comprising 11 transradial amputees, and a MYO armband amputee dataset, comprising 6 transradial amputees. Subject-specific five-fold cross-validation was performed using a Linear Discriminant Analysis (LDA) classifier. Adaptive LSDL consistently improved performance relative to the original fixed-threshold LSDL representation, while fusion improved classification accuracy for 7 of 11 DB3 amputees and 5 of 6 MYO amputees. The largest fusion gains reached 4.39 and 5.60 percentage points for the DB3 and MYO cohorts, respectively. Furthermore, the proposed framework demonstrated competitive performance relative to a lightweight real-time convolutional neural network benchmark while requiring substantially lower computational complexity. These findings indicate that adaptive decomposition and representation-level information fusion provide an effective approach for exploiting subject-specific neuromuscular information while remaining suitable for computationally efficient prosthetic control applications.<\/jats:p>","DOI":"10.3390\/bioengineering13080900","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T11:38:09Z","timestamp":1786361889000},"page":"900","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Adaptive Multi-Representation Fusion Framework for EMG Pattern Recognition in Transradial Amputees"],"prefix":"10.3390","volume":"13","author":[{"given":"Ejay","family":"Nsugbe","sequence":"first","affiliation":[{"name":"Nsugbe Research Labs, Swindon SN1 3LG, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oluwarotimi W.","family":"Samuel","sequence":"additional","affiliation":[{"name":"Data Science Research Centre and School of Computing, College of Science and Engineering, University of Derby, Derby DE22 3AW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1109\/TNSRE.2014.2305111","article-title":"The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges","volume":"22","author":"Farina","year":"2014","journal-title":"IEEE Trans. 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