Academic Journal
Robust decoding of hand movement for mitigating muscle contraction variability from surface EMG signals.
| Title: | Robust decoding of hand movement for mitigating muscle contraction variability from surface EMG signals. |
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| Authors: | Wu Y; School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, Jiangsu, People's Republic of China., Lin D; School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, Jiangsu, People's Republic of China., Lu X; Wuhan Zhongyuan Electronics Group Co. Ltd, Wuhan 430205, Hubei, People's Republic of China., Yan T; School of Physics, Northeast Normal University, Changchun 130024, Jilin, People's Republic of China., Peng P; School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, Jiangsu, People's Republic of China. |
| Source: | Journal of neural engineering [J Neural Eng] 2026 Aug 24; Vol. 23 (4). Date of Electronic Publication: 2026 Aug 24. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Institute of Physics Pub Country of Publication: England NLM ID: 101217933 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-2552 (Electronic) Linking ISSN: 17412552 NLM ISO Abbreviation: J Neural Eng Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol, U.K. : Institute of Physics Pub., 2004- |
| MeSH Terms: | Electromyography*/methods , Hand*/physiology , Muscle Contraction*/physiology , Muscle, Skeletal*/physiology , Pattern Recognition, Automated*/methods, Movement/physiology ; Humans ; Male ; Adult ; Female ; Young Adult ; Algorithms |
| Abstract: | Objective.The widespread application of surface electromyography (sEMG) pattern recognition in artificial limbs largely relies on a robust decoding system that continuously and accurately identifies user hand movement intention. However, the muscle contraction variability significantly distorts the time-frequency components in sEMG signals, thereby weakening the performance of hand movement decoding. This paper aims to propose a novel framework to achieve robust decoding of hand movement for mitigating muscle contraction variability from sEMG signals.Approach.We collect two-channel sEMG signals from eight healthy subjects performing six hand movements under three muscle contraction levels (normal level, medium level, high level). Fifteen popular sEMG features are extracted, and five classic classifiers are employed for decoding hand movements. We analyze how muscle contraction variability degrades hand movement decoding performance and propose a simulation-driven feature mapping framework. It maps abnormal sEMG features from medium and high levels back to normal levels by Feature Denoising Network using simulated sEMG features.Main results.Without the proposed framework, decoding accuracy drops markedly when muscle contraction intensity is switched from normal level to medium and high levels, resulting in accuracy losses of at least 32.32% for observed muscle contraction levels and 37.50% for predicted muscle contraction levels. With the proposed framework, decoding accuracy is significantly improved, with accuracy improvements of at least 31.82% for observed muscle contraction levels and 26.99% for predicted muscle contraction levels.Significance.The paper provides a feasible solution to challenge of muscle contraction variability for robust hand movement decoding from sEMG signals, which holds potential reference value in the development of myoelectric hand prosthesis. (© 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.) |
| Contributed Indexing: | Keywords: Feature Denoising Network; hand movement decoding; muscle contraction variability; simulation-driven feature mapping framework; surface electromyography (sEMG) |
| Entry Date(s): | Date Created: 20260812 Date Completed: 20260824 Latest Revision: 20260824 |
| Update Code: | 20260824 |
| DOI: | 10.1088/1741-2552/ae98b5 |
| PMID: | 42586145 |
| Database: | MEDLINE |
| ISSN: | 1741-2552 |
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| DOI: | 10.1088/1741-2552/ae98b5 |