publication . Article . 2017

Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation

Du, Yu; Jin, Wenguang; Wei, Wentao; Hu, Yu; Geng, Weidong;
Open Access English
  • Published: 01 Feb 2017 Journal: Sensors, volume 17, issue 3 (issn: 1424-8220, eissn: 1424-8220, Copyright policy)
  • Publisher: MDPI AG
Abstract
High-density surface electromyography (HD-sEMG) is to record muscles’ electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition has usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures ...
Subjects
free text keywords: muscle-computer interface, gesture recognition, TP1-1185, electromyography, Chemical technology, domain adaptation, Article
Related Organizations
72 references, page 1 of 5

Saponas, T.S., Tan, D.S., Morris, D., Balakrishnan, R.. Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. : 515-524

Amma, C., Krings, T., Böer, J., Schultz, T.. Advancing muscle-computer interfaces with high-density electromyography. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. : 929-938

Casale, R., Rainoldi, A.. Fatigue and fibromyalgia syndrome: Clinical and neurophysiologic pattern. Best Pract. Res. Clin. Rheumatol.. 2011; 25: 241-247 [PubMed] [DOI]

Masuda, T., Miyano, H., Sadoyama, T.. The propagation of motor unit action potential and the location of neuromuscular junction investigated by surface electrode arrays. Electroencephalogr. Clin. Neurophysiol.. 1983; 55: 594-600 [PubMed] [DOI]

Yamada, M., Kumagai, K., Uchiyama, A.. The distribution and propagation pattern of motor unit action potentials studied by multi-channel surface EMG. Electroencephalogr. Clin. Neurophysiol.. 1987; 67: 395-401 [PubMed] [DOI]

Rojas-Martínez, M., Mañanas, M.A., Alonso, J.F.. High-density surface EMG maps from upper-arm and forearm muscles. J. Neuroeng. Rehabil.. 2012; 9: 1 [OpenAIRE] [PubMed] [DOI]

Rojas-Martínez, M., Mañanas, M., Alonso, J., Merletti, R.. Identification of isometric contractions based on high density EMG maps. J. Electromyogr. Kinesiol.. 2013; 23: 33-42 [OpenAIRE] [PubMed] [DOI]

Zhang, X., Zhou, P.. High-density myoelectric pattern recognition toward improved stroke rehabilitation. IEEE Trans. Biomed. Eng.. 2012; 59: 1649-1657 [PubMed] [DOI]

Stango, A., Negro, F., Farina, D.. Spatial correlation of high density EMG signals provides features robust to electrode number and shift in pattern recognition for myocontrol. IEEE Trans. Neural Syst. Rehabil. Eng.. 2015; 23: 189-198 [PubMed] [DOI]

Castellini, C., van der Smagt, P.. Surface EMG in advanced hand prosthetics. Biol. Cybern.. 2009; 100: 35-47 [OpenAIRE] [PubMed] [DOI]

Farina, D., Jiang, N., Rehbaum, H., Holobar, A., Graimann, B., Dietl, H., Aszmann, O.C.. The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges. IEEE Trans. Neural Syst. Rehabil. Eng.. 2014; 22: 797-809 [OpenAIRE] [PubMed] [DOI]

Hargrove, L., Englehart, K., Hudgins, B.. A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control. Biomed. Signal Process. Control. 2008; 3: 175-180 [DOI]

Boschmann, A., Platzner, M.. Reducing classification accuracy degradation of pattern recognition based myoelectric control caused by electrode shift using a high density electrode array. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). : 4324-4327

Ju, P., Kaelbling, L.P., Singer, Y.. State-based classification of finger gestures from electromyographic signals. Proceedings of the International Conference on Machine Learning. : 439-446

Khushaba, R.N.. Correlation analysis of electromyogram signals for multiuser myoelectric interfaces. IEEE Trans. Neural Syst. Rehabil. Eng.. 2014; 22: 745-755 [PubMed] [DOI]

72 references, page 1 of 5
Abstract
High-density surface electromyography (HD-sEMG) is to record muscles’ electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition has usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures ...
Subjects
free text keywords: muscle-computer interface, gesture recognition, TP1-1185, electromyography, Chemical technology, domain adaptation, Article
Related Organizations
72 references, page 1 of 5

Saponas, T.S., Tan, D.S., Morris, D., Balakrishnan, R.. Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. : 515-524

Amma, C., Krings, T., Böer, J., Schultz, T.. Advancing muscle-computer interfaces with high-density electromyography. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. : 929-938

Casale, R., Rainoldi, A.. Fatigue and fibromyalgia syndrome: Clinical and neurophysiologic pattern. Best Pract. Res. Clin. Rheumatol.. 2011; 25: 241-247 [PubMed] [DOI]

Masuda, T., Miyano, H., Sadoyama, T.. The propagation of motor unit action potential and the location of neuromuscular junction investigated by surface electrode arrays. Electroencephalogr. Clin. Neurophysiol.. 1983; 55: 594-600 [PubMed] [DOI]

Yamada, M., Kumagai, K., Uchiyama, A.. The distribution and propagation pattern of motor unit action potentials studied by multi-channel surface EMG. Electroencephalogr. Clin. Neurophysiol.. 1987; 67: 395-401 [PubMed] [DOI]

Rojas-Martínez, M., Mañanas, M.A., Alonso, J.F.. High-density surface EMG maps from upper-arm and forearm muscles. J. Neuroeng. Rehabil.. 2012; 9: 1 [OpenAIRE] [PubMed] [DOI]

Rojas-Martínez, M., Mañanas, M., Alonso, J., Merletti, R.. Identification of isometric contractions based on high density EMG maps. J. Electromyogr. Kinesiol.. 2013; 23: 33-42 [OpenAIRE] [PubMed] [DOI]

Zhang, X., Zhou, P.. High-density myoelectric pattern recognition toward improved stroke rehabilitation. IEEE Trans. Biomed. Eng.. 2012; 59: 1649-1657 [PubMed] [DOI]

Stango, A., Negro, F., Farina, D.. Spatial correlation of high density EMG signals provides features robust to electrode number and shift in pattern recognition for myocontrol. IEEE Trans. Neural Syst. Rehabil. Eng.. 2015; 23: 189-198 [PubMed] [DOI]

Castellini, C., van der Smagt, P.. Surface EMG in advanced hand prosthetics. Biol. Cybern.. 2009; 100: 35-47 [OpenAIRE] [PubMed] [DOI]

Farina, D., Jiang, N., Rehbaum, H., Holobar, A., Graimann, B., Dietl, H., Aszmann, O.C.. The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges. IEEE Trans. Neural Syst. Rehabil. Eng.. 2014; 22: 797-809 [OpenAIRE] [PubMed] [DOI]

Hargrove, L., Englehart, K., Hudgins, B.. A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control. Biomed. Signal Process. Control. 2008; 3: 175-180 [DOI]

Boschmann, A., Platzner, M.. Reducing classification accuracy degradation of pattern recognition based myoelectric control caused by electrode shift using a high density electrode array. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). : 4324-4327

Ju, P., Kaelbling, L.P., Singer, Y.. State-based classification of finger gestures from electromyographic signals. Proceedings of the International Conference on Machine Learning. : 439-446

Khushaba, R.N.. Correlation analysis of electromyogram signals for multiuser myoelectric interfaces. IEEE Trans. Neural Syst. Rehabil. Eng.. 2014; 22: 745-755 [PubMed] [DOI]

72 references, page 1 of 5
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publication . Article . 2017

Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation

Du, Yu; Jin, Wenguang; Wei, Wentao; Hu, Yu; Geng, Weidong;