Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Online and Cross-User Finger Movement Pattern Recognition by Decoding Neural Drive Information from Surface Electromyogram. |
| Συγγραφείς: |
Zhao H; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230002, P. R. China., Liu Y; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230002, P. R. China., Li X; School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, P. R. China., Chen X; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230002, P. R. China., Zhang X; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230002, P. R. China. |
| Πηγή: |
International journal of neural systems [Int J Neural Syst] 2025 Apr; Vol. 35 (4), pp. 2550014. Date of Electronic Publication: 2025 Feb 04. |
| Τύπος έκδοσης: |
Journal Article |
| Γλώσσα: |
English |
| Στοιχεία περιοδικού: |
Publisher: World Scientific Pub. Co Country of Publication: Singapore NLM ID: 9100527 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1793-6462 (Electronic) Linking ISSN: 01290657 NLM ISO Abbreviation: Int J Neural Syst Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: Singapore ; Teaneck, N.J. : World Scientific Pub. Co., c1989- |
| Ιατρικοί όροι (MeSH): |
Electromyography*/methods , Fingers*/physiology , Pattern Recognition, Automated*/methods , Muscle, Skeletal*/physiology , Pattern Recognition, Physiological*/physiology, Movement/physiology ; Humans ; Male ; Adult ; Female ; Young Adult ; Deep Learning |
| Περίληψη: |
Cross-user variability is a well-known challenge that leads to severe performance degradation and impacts the robustness of practical myoelectric control systems. To address this issue, a novel method for myoelectric recognition of finger movement patterns is proposed by incorporating a neural decoding approach with unsupervised domain adaption (UDA) learning. In our method, the neural decoding approach is implemented by extracting microscopic features characterizing individual motor unit (MU) activities obtained from a two-stage online surface electromyogram (SEMG) decomposition. A specific deep learning model is designed and initially trained using labeled data from a set of existing users. The model can update adaptively when recognizing the movement patterns of a new user. The final movement pattern was determined by a fuzzy weighted decision strategy. SEMG signals were collected from the finger extensor muscles of 15 subjects to detect seven dexterous finger-movement patterns. The proposed method achieved a movement pattern recognition accuracy of ([Formula: see text])% over seven movements under cross-user testing scenarios, much higher than that of the conventional methods using global SEMG features. Our study presents a novel robust myoelectric pattern recognition approach at a fine-grained MU level, with wide applications in neural interface and prosthesis control. |
| Contributed Indexing: |
Keywords: Myoelectric pattern recognition; cross-user; motor unit; online SEMG decomposition |
| Entry Date(s): |
Date Created: 20250205 Date Completed: 20250506 Latest Revision: 20250526 |
| Update Code: |
20260130 |
| DOI: |
10.1142/S0129065725500145 |
| PMID: |
39907499 |
| Βάση Δεδομένων: |
MEDLINE |