publication . Article . 2018

A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition.

Yu Hu; Yongkang Wong; Wentao Wei; Yu Du; Mohan S. Kankanhalli; Weidong Geng;
Open Access English
  • Published: 30 Oct 2018 Journal: PLoS ONE, volume 13, issue 10 (issn: 1932-6203, Copyright policy)
  • Publisher: Public Library of Science (PLoS)
Abstract
The surface electromyography (sEMG)-based gesture recognition with deep learning approach plays an increasingly important role in human-computer interaction. Existing deep learning architectures are mainly based on Convolutional Neural Network (CNN) architecture which captures spatial information of electromyogram signal. Motivated by the sequential nature of electromyogram signal, we propose an attention-based hybrid CNN and RNN (CNN-RNN) architecture to better capture temporal properties of electromyogram signal for gesture recognition problem. Moreover, we present a new sEMG image representation method based on a traditional feature vector which enables deep ...
Persistent Identifiers
Subjects
free text keywords: Research Article, Research and Analysis Methods, Imaging Techniques, Bioassays and Physiological Analysis, Electrophysiological Techniques, Muscle Electrophysiology, Electromyography, Computer and Information Sciences, Artificial Intelligence, Machine Learning, Deep Learning, Engineering and Technology, Signal Processing, Speech Signal Processing, Database and Informatics Methods, Computer Architecture, Signal Filtering, Network Analysis, Signaling Networks, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine, lcsh:Medicine, lcsh:R, lcsh:Science, lcsh:Q, Image processing, Convolutional neural network, Signal processing, Deep learning, Feature vector, Pattern recognition, Artificial neural network, Gesture recognition, Artificial intelligence, business.industry, business, Filter (signal processing), Computer science
61 references, page 1 of 5

1 Reaz MB, Hussain M, Mohd-Yasin F. Techniques of EMG signal analysis: detection, processing, classification and applications. Biol ogical Procedures Online. 2006;8(1):11–35. 10.1251/bpo124 16799694 [OpenAIRE] [PubMed] [DOI]

2 Hakonen M, Piitulainen H, Visala A. Current state of digital signal processing in myoelectric interfaces and related applications. Biomed Signal Process Control. 2015;18:334–359. 10.1016/j.bspc.2015.02.009 [OpenAIRE] [DOI]

3 Karlik B, Tokhi MO, Alci M. A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis. IEEE Transactions on Biomedical Engineering. 2003;50:1255–1261. 10.1109/TBME.2003.818469 14619995 [PubMed] [DOI]

4 Moon I, Lee M, Ryu J, Mun M. Intelligent robotic wheelchair with EMG-, gesture-, and voice-based interfaces. In: IEEE/RSJ International Conference on Intelligent Robots and Systems; 2003. p. 3453–3458.

5 Zhang X, Wang X, Wang B, Sugi T, Nakamura M. Meal assistance system operated by electromyogram (EMG) signals: Movement onset detection with adaptive threshold. International Journal of Control, Automation and Systems. 2010;8:392–397.

6 Rosen J, Fuchs MB, Arcan M. Performances of Hill-Type and Neural Network Muscle models-Toward a Myosignal-Based Exoskeleton. Computers and Biomedical Research. 1999;32:415–439. 10.1006/cbmr.1999.1524 10529300 [OpenAIRE] [PubMed] [DOI]

7 Lyons GM, Sharma P, Baker M, O’Malley S, Shanahan A. A computer game-based EMG biofeedback system for muscle rehabilitation. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2003. p. 1625–1628.

8 van Dijk L, van der Sluis CK, van Dijk HW, Bongers RM. Learning an EMG Controlled Game: Task-Specific Adaptations and Transfer. PLOS ONE. 2016;11(8):1–14. 10.1371/journal.pone.0160817 [OpenAIRE] [DOI]

9 Asai Y, Tateyama S, Nomura T. Learning an Intermittent Control Strategy for Postural Balancing Using an EMG-Based Human-Computer Interface. PLOS ONE. 2013;8(5):1–19. 10.1371/journal.pone.0062956 [OpenAIRE] [DOI]

10 Jorgensen C, Dusan S. Speech interfaces based upon surface electromyography. Speech Communication. 2010;52(4):354–366. 10.1016/j.specom.2009.11.003 [OpenAIRE] [DOI]

11 Hudgins B, Parker P, Scott RN. A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering. 1993;40(1):82–94. 10.1109/10.204774 8468080 [OpenAIRE] [PubMed] [DOI]

12 Du YC, Lin CH, Shyu LY, Chen T. Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis. Expert Syst Appl. 2010;37(6):4283–4291. 10.1016/j.eswa.2009.11.072 [OpenAIRE] [DOI]

13 Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Systems with Applications. 2012;39(8):7420–7431. 10.1016/j.eswa.2012.01.102 [OpenAIRE] [DOI]

14 Doswald A, Carri no F, Ringeval F. Advanced Processing of sEMG Signals for User Independent Gesture Recognition. In: Mediterranean Conference on Medical and Biological Engineering and Computing; 2014. p. 758–761.

15 Khushaba RN, Al-Timemy AH, Al-Ani A, Al-Jumaily A. A Framework of Temporal-Spatial Descriptors-Based Feature Extraction for Improved Myoelectric Pattern Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017;25(10):1821–1831. 10.1109/TNSRE.2017.2687520 28358690 [OpenAIRE] [PubMed] [DOI]

61 references, page 1 of 5
Abstract
The surface electromyography (sEMG)-based gesture recognition with deep learning approach plays an increasingly important role in human-computer interaction. Existing deep learning architectures are mainly based on Convolutional Neural Network (CNN) architecture which captures spatial information of electromyogram signal. Motivated by the sequential nature of electromyogram signal, we propose an attention-based hybrid CNN and RNN (CNN-RNN) architecture to better capture temporal properties of electromyogram signal for gesture recognition problem. Moreover, we present a new sEMG image representation method based on a traditional feature vector which enables deep ...
Persistent Identifiers
Subjects
free text keywords: Research Article, Research and Analysis Methods, Imaging Techniques, Bioassays and Physiological Analysis, Electrophysiological Techniques, Muscle Electrophysiology, Electromyography, Computer and Information Sciences, Artificial Intelligence, Machine Learning, Deep Learning, Engineering and Technology, Signal Processing, Speech Signal Processing, Database and Informatics Methods, Computer Architecture, Signal Filtering, Network Analysis, Signaling Networks, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine, lcsh:Medicine, lcsh:R, lcsh:Science, lcsh:Q, Image processing, Convolutional neural network, Signal processing, Deep learning, Feature vector, Pattern recognition, Artificial neural network, Gesture recognition, Artificial intelligence, business.industry, business, Filter (signal processing), Computer science
61 references, page 1 of 5

1 Reaz MB, Hussain M, Mohd-Yasin F. Techniques of EMG signal analysis: detection, processing, classification and applications. Biol ogical Procedures Online. 2006;8(1):11–35. 10.1251/bpo124 16799694 [OpenAIRE] [PubMed] [DOI]

2 Hakonen M, Piitulainen H, Visala A. Current state of digital signal processing in myoelectric interfaces and related applications. Biomed Signal Process Control. 2015;18:334–359. 10.1016/j.bspc.2015.02.009 [OpenAIRE] [DOI]

3 Karlik B, Tokhi MO, Alci M. A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis. IEEE Transactions on Biomedical Engineering. 2003;50:1255–1261. 10.1109/TBME.2003.818469 14619995 [PubMed] [DOI]

4 Moon I, Lee M, Ryu J, Mun M. Intelligent robotic wheelchair with EMG-, gesture-, and voice-based interfaces. In: IEEE/RSJ International Conference on Intelligent Robots and Systems; 2003. p. 3453–3458.

5 Zhang X, Wang X, Wang B, Sugi T, Nakamura M. Meal assistance system operated by electromyogram (EMG) signals: Movement onset detection with adaptive threshold. International Journal of Control, Automation and Systems. 2010;8:392–397.

6 Rosen J, Fuchs MB, Arcan M. Performances of Hill-Type and Neural Network Muscle models-Toward a Myosignal-Based Exoskeleton. Computers and Biomedical Research. 1999;32:415–439. 10.1006/cbmr.1999.1524 10529300 [OpenAIRE] [PubMed] [DOI]

7 Lyons GM, Sharma P, Baker M, O’Malley S, Shanahan A. A computer game-based EMG biofeedback system for muscle rehabilitation. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2003. p. 1625–1628.

8 van Dijk L, van der Sluis CK, van Dijk HW, Bongers RM. Learning an EMG Controlled Game: Task-Specific Adaptations and Transfer. PLOS ONE. 2016;11(8):1–14. 10.1371/journal.pone.0160817 [OpenAIRE] [DOI]

9 Asai Y, Tateyama S, Nomura T. Learning an Intermittent Control Strategy for Postural Balancing Using an EMG-Based Human-Computer Interface. PLOS ONE. 2013;8(5):1–19. 10.1371/journal.pone.0062956 [OpenAIRE] [DOI]

10 Jorgensen C, Dusan S. Speech interfaces based upon surface electromyography. Speech Communication. 2010;52(4):354–366. 10.1016/j.specom.2009.11.003 [OpenAIRE] [DOI]

11 Hudgins B, Parker P, Scott RN. A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering. 1993;40(1):82–94. 10.1109/10.204774 8468080 [OpenAIRE] [PubMed] [DOI]

12 Du YC, Lin CH, Shyu LY, Chen T. Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis. Expert Syst Appl. 2010;37(6):4283–4291. 10.1016/j.eswa.2009.11.072 [OpenAIRE] [DOI]

13 Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Systems with Applications. 2012;39(8):7420–7431. 10.1016/j.eswa.2012.01.102 [OpenAIRE] [DOI]

14 Doswald A, Carri no F, Ringeval F. Advanced Processing of sEMG Signals for User Independent Gesture Recognition. In: Mediterranean Conference on Medical and Biological Engineering and Computing; 2014. p. 758–761.

15 Khushaba RN, Al-Timemy AH, Al-Ani A, Al-Jumaily A. A Framework of Temporal-Spatial Descriptors-Based Feature Extraction for Improved Myoelectric Pattern Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017;25(10):1821–1831. 10.1109/TNSRE.2017.2687520 28358690 [OpenAIRE] [PubMed] [DOI]

61 references, page 1 of 5
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