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Cited by 94Muscle activation and electromyography studiesHand Gesture Recognition SystemsDomain Adaptation and Few-Shot Learning

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Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks

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Ketyko, Istvan, Kovacs, Ferenc, Varga, Krisztian Zsolt·2019·2019 International Joint Conference on Neural Networks (IJCNN)
DOI 10.1109/ijcnn.2019.8852018
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Abstract

Surface Electromyography (sEMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approximate the domain shift for recognition accuracy enhancement. Analysis performed on sparse and High-Density (HD) sEMG public datasets validate that our approach outperforms state-of-the-art methods.

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References 13 · Cited by 94

Research works printed but not linked: 9

  1. yun, 2013, Sign Language Recognition System using SEMG and Hidden Markov Model, International Conference on Mathematical Methods Computational Techniques and Intelligent Systems, 50
  2. kingma, 2014, Adam A method for stochastic optimization
  3. konrad, 2006, The ABC of EMG A Practical Introduction to Kinesiological Electromyography
  4. 0, Christopher Olah Understanding LSTM Networks
  5. pascanu, 2013, On the Difficulty of Training Recurrent Neural Networks
  6. 0, EMG dataset in lower limb
  7. 0, CapgMyo A High Density Surface Electromyography Database for Gesture Recognition
  8. andrew, 2013, Deep Canonical Correlation Analysis, Proceedings of the 30th International Conference on Machine Learning
  9. chung, 2014, Empirical evaluation of gated recurrent neural networks on sequence modeling

Metadata

Authors
Ketyko, I., Kovacs, F., & Varga, K. Z.
Affiliations
Eötvös Loránd University (HU)
Venue
2019 International Joint Conference on Neural Networks (IJCNN)
Publisher
IEEE
Published
2019-07
Pages
1-7
ISSN
2161-4407 (ISSN-L)
DOI
10.1109/ijcnn.2019.8852018
OpenAlex
W2911953725
Cited by
94
References
13
Citations by year
2026: 9 · 2025: 15 · 2024: 18 · 2023: 20 · 2022: 19 · 2021: 8 · 2020: 3 · 2019: 2
FWCI
16.61
Type
conference paper
Language
en
Open access
Licence
Locations
  • International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
surface electromyography, gesture recognition, domain adaptation, interindividual variability, inter-session variability, recurrent neural networks, high-density sEMG, domain shift

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CoChat. (2026). Citation check: Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks. https://app.cochat.ai/papers/10.1109/ijcnn.2019.8852018
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Ketyko, I., Kovacs, F., & Varga, K. Z. (2019). Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks. 2019 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2019.8852018