Electromyography-based Gesture Recognition
Electromyography-Based Gesture Recognition is a pattern recognition methodology that decodes neuromuscular activity captured through surface electromyography signals to infer and classify specific hand or arm gestures through machine learning or signal processing techniques.
436
Publications
23.5K
Citations
1.3K
Authors
434
Institutions
Publications per year
2017–2026
340
Parents
436
Gesture recognition by instantaneous surface EMG images
Weidong Geng, Yu Du, Wenguang Jin et al. · Scientific Reports · 2016 · 592 citations · Full text
A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition
Yu Hu, Yongkang Wong, Wentao Wei et al. · PLoS ONE · 2018 · 396 citations · Full text
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation
Yu Du, Wenguang Jin, Wentao Wei et al. · Sensors · 2017 · 363 citations · Full text
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434
ETH Zurich
Zurich, Switzerland
15 h-index
41 publications
3.5K citations

Chinese Academy of Sciences
Beijing, China
13 h-index
41 publications
1.8K citations
National Polytechnic School
Quito, Ecuador
12 h-index
58 publications
3.2K citations

Shanghai Jiao Tong University
Shanghai, China
11 h-index
36 publications
3K citations

University of Bologna
Bologna, Italy
11 h-index
32 publications
1.9K citations

Shanghai University
Shanghai, China
11 h-index
33 publications
1.4K citations

Concordia University
Montreal, Canada
10 h-index
36 publications
1.5K citations
University of Portsmouth
Portsmouth, United Kingdom
9 h-index
13 publications
527 citations
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37 publications · 2.4K citations
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16 publications · 1.2K citations
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17 publications · 1.6K citations
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10 publications · 492 citations
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6 publications · 201 citations
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