Automatic discovery of resource-restricted Convolutional Neural Network topologies for myoelectric pattern recognition

Convolutional Neural Networks (CNNs) have been subject to extensive attention in the pattern recognition literature due to unprecedented performance in tasks of information extraction from unstructured data. Whereas available methods for supervised training of a CNN with a given network topology are...

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Published in:	Computers in biology and medicine Vol. 120; p. 103723
Main Authors:	Olsson, Alexander E., Björkman, Anders, Antfolk, Christian
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.05.2020 Elsevier Limited
Subjects:	Algorithms Artificial neural networks Bioinformatics (Computational Biology) Bioinformatik (Beräkningsbiologi) Collating Computer and Information Sciences Computer applications Convolutional neural networks Data- och informationsvetenskap (Datateknik) Deep learning Discriminant analysis Electromyography Embedded systems Evolutionary algorithms Information retrieval Interfaces Internal Medicine Iterative methods Machine learning Medical research Model selection Muscle-computer interfaces Muscles Myoelectric control Myoelectric pattern recognition Myoelectricity Natural Sciences Naturvetenskap Network topologies Neural networks Other Pattern recognition Problem solving Prostheses Unstructured data Myoelectric control Deep learning Model selection Muscle-computer interfaces Machine learning Electromyography Convolutional neural networks Myoelectric pattern recognition
ISSN:	0010-4825, 1879-0534, 1879-0534
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Abstract	Convolutional Neural Networks (CNNs) have been subject to extensive attention in the pattern recognition literature due to unprecedented performance in tasks of information extraction from unstructured data. Whereas available methods for supervised training of a CNN with a given network topology are well-defined with rigorous theoretical justification, procedures for the initial selection of topology are currently not. Work incorporating selection of the CNN topology has instead substantially been guided by the domain-specific expertise of the creator(s), followed by iterative improvement via empirical evaluation. This limitation of methodology is restricting in the pursuit of naturally controlled muscle-computer interfaces, where CNNs have been identified as a promising research avenue but effective topology selection heuristics are lacking. With the goal of mitigating ambiguities in topology selection, this paper presents a systematic approach wherein we apply a novel evolutionary algorithm to search a space of candidate topologies. Furthermore, we constrain the search-space by excluding topologies with excessive inference-time computational complexity, making the obtained results implementable in embedded systems. In contrast to manual topology design, our algorithm requires the user to only specify a relatively small set of intuitive hyperparameters. To validate our approach, we use it in order to create topologies for myoelectric pattern recognition via movement decoding of surface electromyography signals. By collating offline classification accuracies obtained from experiments on a collection of publicly available databases, we demonstrate that our method generates computationally lightweight topologies with performance comparable to those of available alternatives. •A meta-learning algorithm is introduced to generate CNN topologies suitable for EMG gesture classification.•Only classifiers with feasible time complexity and memory footprint are considered.•The method is demonstrated by automatically generating a novel CNN topology.•The resulting network is evaluated on a set of publicly available EMG databases.•Classification performance was comparable to that of costlier CNNs introduced previously.
AbstractList	Convolutional Neural Networks (CNNs) have been subject to extensive attention in the pattern recognition literature due to unprecedented performance in tasks of information extraction from unstructured data. Whereas available methods for supervised training of a CNN with a given network topology are well-defined with rigorous theoretical justification, procedures for the initial selection of topology are currently not. Work incorporating selection of the CNN topology has instead substantially been guided by the domain-specific expertise of the creator(s), followed by iterative improvement via empirical evaluation. This limitation of methodology is restricting in the pursuit of naturally controlled muscle-computer interfaces, where CNNs have been identified as a promising research avenue but effective topology selection heuristics are lacking. With the goal of mitigating ambiguities in topology selection, this paper presents a systematic approach wherein we apply a novel evolutionary algorithm to search a space of candidate topologies. Furthermore, we constrain the search-space by excluding topologies with excessive inference-time computational complexity, making the obtained results implementable in embedded systems. In contrast to manual topology design, our algorithm requires the user to only specify a relatively small set of intuitive hyperparameters. To validate our approach, we use it in order to create topologies for myoelectric pattern recognition via movement decoding of surface electromyography signals. By collating offline classification accuracies obtained from experiments on a collection of publicly available databases, we demonstrate that our method generates computationally lightweight topologies with performance comparable to those of available alternatives. •A meta-learning algorithm is introduced to generate CNN topologies suitable for EMG gesture classification.•Only classifiers with feasible time complexity and memory footprint are considered.•The method is demonstrated by automatically generating a novel CNN topology.•The resulting network is evaluated on a set of publicly available EMG databases.•Classification performance was comparable to that of costlier CNNs introduced previously. Convolutional Neural Networks (CNNs) have been subject to extensive attention in the pattern recognition literature due to unprecedented performance in tasks of information extraction from unstructured data. Whereas available methods for supervised training of a CNN with a given network topology are well-defined with rigorous theoretical justification, procedures for the initial selection of topology are currently not. Work incorporating selection of the CNN topology has instead substantially been guided by the domain-specific expertise of the creator(s), followed by iterative improvement via empirical evaluation. This limitation of methodology is restricting in the pursuit of naturally controlled muscle-computer interfaces, where CNNs have been identified as a promising research avenue but effective topology selection heuristics are lacking. With the goal of mitigating ambiguities in topology selection, this paper presents a systematic approach wherein we apply a novel evolutionary algorithm to search a space of candidate topologies. Furthermore, we constrain the search-space by excluding topologies with excessive inference-time computational complexity, making the obtained results implementable in embedded systems. In contrast to manual topology design, our algorithm requires the user to only specify a relatively small set of intuitive hyperparameters. To validate our approach, we use it in order to create topologies for myoelectric pattern recognition via movement decoding of surface electromyography signals. By collating offline classification accuracies obtained from experiments on a collection of publicly available databases, we demonstrate that our method generates computationally lightweight topologies with performance comparable to those of available alternatives. Convolutional Neural Networks (CNNs) have been subject to extensive attention in the pattern recognition literature due to unprecedented performance in tasks of information extraction from unstructured data. Whereas available methods for supervised training of a CNN with a given network topology are well-defined with rigorous theoretical justification, procedures for the initial selection of topology are currently not. Work incorporating selection of the CNN topology has instead substantially been guided by the domain-specific expertise of the creator(s), followed by iterative improvement via empirical evaluation. This limitation of methodology is restricting in the pursuit of naturally controlled muscle-computer interfaces, where CNNs have been identified as a promising research avenue but effective topology selection heuristics are lacking. With the goal of mitigating ambiguities in topology selection, this paper presents a systematic approach wherein we apply a novel evolutionary algorithm to search a space of candidate topologies. Furthermore, we constrain the search-space by excluding topologies with excessive inference-time computational complexity, making the obtained results implementable in embedded systems. In contrast to manual topology design, our algorithm requires the user to only specify a relatively small set of intuitive hyperparameters. To validate our approach, we use it in order to create topologies for myoelectric pattern recognition via movement decoding of surface electromyography signals. By collating offline classification accuracies obtained from experiments on a collection of publicly available databases, we demonstrate that our method generates computationally lightweight topologies with performance comparable to those of available alternatives.Convolutional Neural Networks (CNNs) have been subject to extensive attention in the pattern recognition literature due to unprecedented performance in tasks of information extraction from unstructured data. Whereas available methods for supervised training of a CNN with a given network topology are well-defined with rigorous theoretical justification, procedures for the initial selection of topology are currently not. Work incorporating selection of the CNN topology has instead substantially been guided by the domain-specific expertise of the creator(s), followed by iterative improvement via empirical evaluation. This limitation of methodology is restricting in the pursuit of naturally controlled muscle-computer interfaces, where CNNs have been identified as a promising research avenue but effective topology selection heuristics are lacking. With the goal of mitigating ambiguities in topology selection, this paper presents a systematic approach wherein we apply a novel evolutionary algorithm to search a space of candidate topologies. Furthermore, we constrain the search-space by excluding topologies with excessive inference-time computational complexity, making the obtained results implementable in embedded systems. In contrast to manual topology design, our algorithm requires the user to only specify a relatively small set of intuitive hyperparameters. To validate our approach, we use it in order to create topologies for myoelectric pattern recognition via movement decoding of surface electromyography signals. By collating offline classification accuracies obtained from experiments on a collection of publicly available databases, we demonstrate that our method generates computationally lightweight topologies with performance comparable to those of available alternatives. AbstractConvolutional Neural Networks (CNNs) have been subject to extensive attention in the pattern recognition literature due to unprecedented performance in tasks of information extraction from unstructured data. Whereas available methods for supervised training of a CNN with a given network topology are well-defined with rigorous theoretical justification, procedures for the initial selection of topology are currently not. Work incorporating selection of the CNN topology has instead substantially been guided by the domain-specific expertise of the creator(s), followed by iterative improvement via empirical evaluation. This limitation of methodology is restricting in the pursuit of naturally controlled muscle-computer interfaces, where CNNs have been identified as a promising research avenue but effective topology selection heuristics are lacking. With the goal of mitigating ambiguities in topology selection, this paper presents a systematic approach wherein we apply a novel evolutionary algorithm to search a space of candidate topologies. Furthermore, we constrain the search-space by excluding topologies with excessive inference-time computational complexity, making the obtained results implementable in embedded systems. In contrast to manual topology design, our algorithm requires the user to only specify a relatively small set of intuitive hyperparameters. To validate our approach, we use it in order to create topologies for myoelectric pattern recognition via movement decoding of surface electromyography signals. By collating offline classification accuracies obtained from experiments on a collection of publicly available databases, we demonstrate that our method generates computationally lightweight topologies with performance comparable to those of available alternatives.
ArticleNumber	103723
Author	Olsson, Alexander E. Björkman, Anders Antfolk, Christian
Author_xml	– sequence: 1 givenname: Alexander E. surname: Olsson fullname: Olsson, Alexander E. email: alexander.olsson@bme.lth.se organization: Dept. of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden – sequence: 2 givenname: Anders surname: Björkman fullname: Björkman, Anders organization: Dept. of Hand Surgery, Lund University, Skåne University Hospital, Malmö, Sweden – sequence: 3 givenname: Christian surname: Antfolk fullname: Antfolk, Christian email: christian.antfolk@bme.lth.se organization: Dept. of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
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Cites_doi	10.3390/sym8120148 10.1016/j.medengphy.2011.11.018 10.1109/TNSRE.2012.2196711 10.1371/journal.pone.0206049 10.1162/neco.1989.1.4.541 10.1007/s13246-011-0079-z 10.1109/TNSRE.2010.2100828 10.1016/j.eswa.2012.01.102 10.1109/TNSRE.2017.2687520 10.1682/JRRD.2010.09.0177 10.1038/s41598-019-43676-8 10.1016/j.eswa.2011.06.043 10.1155/2018/9728264 10.1097/00008526-199600810-00003 10.3390/bdcc2030021 10.1109/TNSRE.2011.2108667 10.1080/17483100701714733 10.1109/TNSRE.2014.2305111 10.1016/j.bspc.2015.02.009 10.3389/fnins.2017.00379 10.1682/JRRD.2011.10.0188 10.3390/s17030458 10.1016/0893-6080(89)90020-8 10.1108/01439910810876364 10.5405/jmbe.767 10.3389/fnbot.2016.00009 10.1038/sdata.2014.53 10.1016/j.patrec.2017.12.005 10.1088/1741-2552/ab0e2e 10.1007/s11263-015-0816-y 10.1186/1743-0003-9-85 10.1186/1751-0473-8-11 10.1080/03093640601061265 10.3109/03091902.2016.1167971 10.1615/CritRevBiomedEng.v30.i456.80
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Keywords	Myoelectric control Deep learning Model selection Muscle-computer interfaces Machine learning Electromyography Convolutional neural networks Myoelectric pattern recognition
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References	Connolly (bib23) 2008; 32 Alkan, Günay (bib32) 2012; 39 He, Zhang, Ren, Sun (bib8) 2016 Hu, Wong, Wei, Du, Kankanhalli, Geng (bib43) 2018; 13 Shim, An, Lee, Lee, Min, Lee (bib46) 2016; 8 Chen, Zhang, Zhao, Yang, Lantz, Wang (bib34) 2017 Snoek, Larochelle, Adams (bib54) 2012; 2 Huang, Li, Enz, Koch, Justiz, Antfolk (bib33) 2016 Geethanjali, Ray (bib28) 2011; 34 Farina, Jiang, Rehbaum, Holobar, Graimann, Dietl, Aszmann (bib16) 2014; 22 Moons, Bankman, Verhelst (bib66) 2019 Miikkulainen, Liang, Meyerson, Rawal, Fink, Francon, Raju, Shahrzad, Navruzyan, Duffy, Hodjat (bib53) 2018 Biddiss, Beaton, Chau (bib17) 2007; 2 Jimenez-Fabian, Verlinden (bib15) 2012; 34 Ko, Peddinti, Povey, Seltzer, Khudanpur (bib9) 2017 Saikia, Mazumdar, Sahai, Paul, Bhatia, Verma, Rohilla (bib20) 2016; 40 Phinyomark, Scheme (bib12) 2018; 2 Ortiz-Catalan, Brånemark, Håkansson (bib67) 2013; 8 Bäck (bib58) 1996 Phinyomark, Limsakul, Phukpattaranont (bib27) 2009 Rojas-Martnex, Mañanas, Alonso (bib50) 2012; 9 Abadi, Barham, Chen, Chen, Davis, Dean, Devin, Ghemawat, Irving, Isard, Kudlur, Levenberg, Monga, Moore, Murray, Steiner, Tucker, Vasudevan, Warden, Wicke, Yu, Zheng (bib63) 2016 Pylatiuk, Schulz, Döderlein (bib18) 2007; 31 Fougner, Stavdahl, Kyberd, Losier, Parker (bib22) 2012; 20 Phinyomark, Phukpattaranont, Limsakul (bib37) 2012; 39 bib5 Atkins, Heard, Donovan (bib19) 1996; 8 Smith, Hargrove, Lock, Kuiken (bib65) 2011; 19 Han, Pool, Tran, Dally (bib56) 2015; 1 Mills (bib13) 2005; 76 Ioffe, Szegedy (bib61) 2015 Real, Moore, Selle, Saxena, Suematsu, Tan, Le, Kurakin (bib52) 2017; 70 Zhai, Jelfs, Chan, Tin (bib41) 2017; 11 Cipriani, Antfolk, Controzzi, Lundborg, Rosen, Carrozza, Sebelius (bib29) 2011; 90 Collobert, Weston, Bottou, Karlen, Kavukcuoglu, Kuksa (bib10) 2010; 12 Hornik, Stinchcombe, White (bib38) 1989; 2 Du, Jin, Wei, Hu, Geng (bib47) 2017; 17 Scheme, Englehart (bib25) 2011; 48 Zecca, Micera, Carrozza, Dario (bib26) 2002; 30 Kim, Lu, Ma, Kim, Kim, Wang, Wu, Won, Tao, Islam, Yu, Kim, Chowdhury, Ying, Xu, Li, Chung, Keum (bib14) 2011; 333 Atzori, Cognolato, Müller (bib39) 2016; 10 Kingma, Ba (bib62) 2014 Russakovsky, Deng, Su, Krause, Satheesh, Ma, Huang, Karpathy, Khosla, Bernstein, Berg, Fei-Fei (bib4) 2015; 115 Szegedy, Liu, Jia, Sermanet, Reed, Anguelov, Erhan, Vanhoucke, Rabinovich (bib7) 2015 Wallach, Dzamba, Heifets (bib11) 2015 LeCun, Bengio, Hinton (bib2) 2015; 521 Srivastava, Hinton, Krizhevsky, Sutskever, Salakhutdinov (bib60) 2014; 15 Shuman, Duric, Barbara, Lin, Gerber (bib36) 2016; 16 Zoph, Le (bib55) 2016 Goodfellow, Bengio, Courville (bib1) 2016 Hakonen, Piitulainen, Visala (bib24) 2015; 18 Olsson, Sager, Andersson, Björkman, Malešević, Antfolk (bib48) 2019; 9 Krizhevsky, Sutskever, Hinton (bib6) 2012; 25 Ameri, Akhaee, Scheme, Englehart (bib49) 2019; 16 Cortes, Gonzalvo, Kuznetsov, Mohri, Yang (bib51) 2017; 70 Malešević, Markovic, Kanitz, Controzzi, Cipriani, Antfolk (bib35) 2018 Wei, Wong, Du, Hu, Kankanhalli, Geng (bib42) 2017; 119 Stanley, Miikkulainen (bib57) 2002 Kanitz, Antfolk, Cipriani, Sebelius, Carrozza (bib59) 2011 Belter, Segil, Dollar, Weir (bib21) 2013; 50 Antfolk, Cipriani, Controzzi, Carrozza, Lundborg, Rosen, Sebelius (bib30) 2010; 30 Khushaba, Al-Timemy, Al-Ani, Al-Jumaily (bib31) 2017; 25 Atzori, Gijsberts, Castellini, Caputo, Mittaz Hager, Elsig, Giatsidis, Bassetto, Müller (bib64) 2014; 1 Park, Lee (bib44) 2016 LeCun, Boser, Denker, Henderson, Howard, Hubbard, Jackel (bib3) 1989; 1 Geng, Du, Jin, Wei, Hu, Li (bib40) 2016; 15 ur Rehman, Gilani, Waris, Niazi, Slabaugh, Farina, Kamavuako (bib45) 2018; 8 Atzori (10.1016/j.compbiomed.2020.103723_bib64) 2014; 1 Goodfellow (10.1016/j.compbiomed.2020.103723_bib1) 2016 Smith (10.1016/j.compbiomed.2020.103723_bib65) 2011; 19 He (10.1016/j.compbiomed.2020.103723_bib8) 2016 Connolly (10.1016/j.compbiomed.2020.103723_bib23) 2008; 32 Du (10.1016/j.compbiomed.2020.103723_bib47) 2017; 17 Stanley (10.1016/j.compbiomed.2020.103723_bib57) 2002 Belter (10.1016/j.compbiomed.2020.103723_bib21) 2013; 50 Atkins (10.1016/j.compbiomed.2020.103723_bib19) 1996; 8 Zoph (10.1016/j.compbiomed.2020.103723_bib55) LeCun (10.1016/j.compbiomed.2020.103723_bib2) 2015; 521 Saikia (10.1016/j.compbiomed.2020.103723_bib20) 2016; 40 Shim (10.1016/j.compbiomed.2020.103723_bib46) 2016; 8 Atzori (10.1016/j.compbiomed.2020.103723_bib39) 2016; 10 Farina (10.1016/j.compbiomed.2020.103723_bib16) 2014; 22 LeCun (10.1016/j.compbiomed.2020.103723_bib3) 1989; 1 Phinyomark (10.1016/j.compbiomed.2020.103723_bib27) Ameri (10.1016/j.compbiomed.2020.103723_bib49) 2019; 16 Ortiz-Catalan (10.1016/j.compbiomed.2020.103723_bib67) 2013; 8 Fougner (10.1016/j.compbiomed.2020.103723_bib22) 2012; 20 Chen (10.1016/j.compbiomed.2020.103723_bib34) 2017 Abadi (10.1016/j.compbiomed.2020.103723_bib63) 2016 Wallach (10.1016/j.compbiomed.2020.103723_bib11) 2015 Huang (10.1016/j.compbiomed.2020.103723_bib33) 2016 Zhai (10.1016/j.compbiomed.2020.103723_bib41) 2017; 11 Geng (10.1016/j.compbiomed.2020.103723_bib40) 2016; 15 Khushaba (10.1016/j.compbiomed.2020.103723_bib31) 2017; 25 Russakovsky (10.1016/j.compbiomed.2020.103723_bib4) 2015; 115 Cipriani (10.1016/j.compbiomed.2020.103723_bib29) 2011; 90 Hornik (10.1016/j.compbiomed.2020.103723_bib38) 1989; 2 Wei (10.1016/j.compbiomed.2020.103723_bib42) 2017; 119 Han (10.1016/j.compbiomed.2020.103723_bib56) 2015; 1 Park (10.1016/j.compbiomed.2020.103723_bib44) 2016 Snoek (10.1016/j.compbiomed.2020.103723_bib54) 2012; 2 Malešević (10.1016/j.compbiomed.2020.103723_bib35) 2018 Alkan (10.1016/j.compbiomed.2020.103723_bib32) 2012; 39 Miikkulainen (10.1016/j.compbiomed.2020.103723_bib53) 2018 Ko (10.1016/j.compbiomed.2020.103723_bib9) 2017 Hakonen (10.1016/j.compbiomed.2020.103723_bib24) 2015; 18 ur Rehman (10.1016/j.compbiomed.2020.103723_bib45) 2018; 8 Kingma (10.1016/j.compbiomed.2020.103723_bib62) Srivastava (10.1016/j.compbiomed.2020.103723_bib60) 2014; 15 Phinyomark (10.1016/j.compbiomed.2020.103723_bib37) 2012; 39 Zecca (10.1016/j.compbiomed.2020.103723_bib26) 2002; 30 Mills (10.1016/j.compbiomed.2020.103723_bib13) 2005; 76 Ioffe (10.1016/j.compbiomed.2020.103723_bib61) Real (10.1016/j.compbiomed.2020.103723_bib52) 2017; 70 Scheme (10.1016/j.compbiomed.2020.103723_bib25) 2011; 48 Shuman (10.1016/j.compbiomed.2020.103723_bib36) 2016; 16 Collobert (10.1016/j.compbiomed.2020.103723_bib10) 2010; 12 Biddiss (10.1016/j.compbiomed.2020.103723_bib17) 2007; 2 Kim (10.1016/j.compbiomed.2020.103723_bib14) 2011; 333 Szegedy (10.1016/j.compbiomed.2020.103723_bib7) 2015 Pylatiuk (10.1016/j.compbiomed.2020.103723_bib18) 2007; 31 Olsson (10.1016/j.compbiomed.2020.103723_bib48) 2019; 9 Geethanjali (10.1016/j.compbiomed.2020.103723_bib28) 2011; 34 Cortes (10.1016/j.compbiomed.2020.103723_bib51) 2017; 70 Moons (10.1016/j.compbiomed.2020.103723_bib66) 2019 Bäck (10.1016/j.compbiomed.2020.103723_bib58) 1996 Antfolk (10.1016/j.compbiomed.2020.103723_bib30) 2010; 30 Jimenez-Fabian (10.1016/j.compbiomed.2020.103723_bib15) 2012; 34 Krizhevsky (10.1016/j.compbiomed.2020.103723_bib6) 2012; 25 Kanitz (10.1016/j.compbiomed.2020.103723_bib59) 2011 Rojas-Martnex (10.1016/j.compbiomed.2020.103723_bib50) 2012; 9 Hu (10.1016/j.compbiomed.2020.103723_bib43) 2018; 13 Phinyomark (10.1016/j.compbiomed.2020.103723_bib12) 2018; 2
References_xml	– volume: 34 start-page: 419 year: 2011 end-page: 427 ident: bib28 article-title: Identification of motion from multi-channel EMG signals for control of prosthetic hand publication-title: Australas. Phys. Eng. Sci. Med. – volume: 8 start-page: 7 year: 2018 ident: bib45 article-title: Stacked sparse autoencoders for EMG-based classification of hand motions: a comparative multi day analyses between surface and intramuscular EMG publication-title: Appl. Sci. – volume: 1 year: 2014 ident: bib64 article-title: Electromyography data for non-invasive naturally-controlled robotic hand prostheses publication-title: Sci. Data – volume: 30 start-page: 459 year: 2002 end-page: 485 ident: bib26 article-title: Control of multifunctional prosthetic hands by processing the electromyographic signal publication-title: Crit. Rev. Biomed. Eng. – volume: 30 start-page: 399 year: 2010 end-page: 406 ident: bib30 article-title: Using EMG for real-time prediction of joint angles of a prosthetic hand equipped with a sensory feedback system publication-title: J. Med. Biol. Eng. – volume: 50 start-page: 599 year: 2013 end-page: 618 ident: bib21 article-title: Mechanical design and performance specifications of anthropomorphic prosthetic hands: a review publication-title: J. Rehabil. Res. Dev. – volume: 13 year: 2018 ident: bib43 article-title: A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition publication-title: PloS One – volume: 15 start-page: 1929 year: 2014 end-page: 1958 ident: bib60 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. – volume: 1 start-page: 541 year: 1989 end-page: 551 ident: bib3 article-title: Backpropagation applied to handwritten zip code recognition publication-title: Neural Comput. – volume: 17 start-page: 3 year: 2017 ident: bib47 article-title: Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation publication-title: Sensors – volume: 20 start-page: 663 year: 2012 end-page: 677 ident: bib22 article-title: Control of upper limb prostheses: terminology and proportional myoelectric control-a review publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 76 start-page: 32 year: 2005 end-page: 35 ident: bib13 article-title: The basics of electromyography publication-title: J. Neurol. Neurosurg. Psychiatry – volume: 8 start-page: 2 year: 1996 end-page: 11 ident: bib19 article-title: Epidemiologic overview of individuals with upper-limb loss and their reported research priorities publication-title: J. Prosthet. Orthot. – start-page: 293 year: 2018 end-page: 312 ident: bib53 article-title: Evolving deep neural networks publication-title: Artificial Intelligence in the Age of Neural Networks and Brain Computing – year: 1996 ident: bib58 article-title: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms – year: 2016 ident: bib44 article-title: Movement intention decoding based on deep learning for multiuser myoelectric interfaces publication-title: Proceedings of BCI – volume: 34 start-page: 397 year: 2012 end-page: 408 ident: bib15 article-title: Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons publication-title: Med. Eng. Phys. – start-page: 11 year: 2017 end-page: 14 ident: bib34 article-title: Hand gesture recognition research based on surface emg sensors and 2d-accelerometers publication-title: Proc. IEEE Int. Sym. Wrbl. Co. – volume: 2 start-page: 2951 year: 2012 end-page: 2959 ident: bib54 article-title: Practical bayesian optimization of machine learning algorithms publication-title: In Proc. Adv. Neural Inf. Process. Syst. – year: 2016 ident: bib33 article-title: EMG pattern recognition using decomposition techniques for constructing multiclass classifiers publication-title: , Singpore – year: 2009 ident: bib27 article-title: A novel feature extraction for robust EMG pattern recognition – volume: 39 start-page: 7420 year: 2012 end-page: 7431 ident: bib37 article-title: Feature reduction and selection for emg signal classification publication-title: Expert Syst. Appl. – year: 2011 ident: bib59 article-title: Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm publication-title: , Boston, USA – volume: 90 start-page: 260 year: 2011 end-page: 270 ident: bib29 article-title: Online myoelectric control of a dexterous hand prosthesis by transradial amputees publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 12 start-page: 2493 year: 2010 end-page: 2537 ident: bib10 article-title: Natural language processing (almost) from scratch publication-title: J. Mach. Learn. Res. – year: 2015 ident: bib11 article-title: AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery – volume: 8 start-page: 11 year: 2013 ident: bib67 article-title: BioPatRec: a modular research platform for the control of artificial limbs based on pattern recognition algorithms publication-title: Source Code Biol. Med. – volume: 31 start-page: 362 year: 2007 end-page: 370 ident: bib18 article-title: Results of an Internet survey of myoelectric prosthetic hand users publication-title: Prosthet. Orthot. Int. – volume: 9 start-page: 85 year: 2012 ident: bib50 article-title: High-density surface EMG maps from upper-arm and forearm muscles publication-title: J. NeuroEng. Rehabil. – volume: 22 start-page: 797 year: 2014 end-page: 809 ident: bib16 article-title: The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 70 start-page: 2902 year: 2017 end-page: 2911 ident: bib52 article-title: Large-scale evolution of image classifiers publication-title: Proc. ICML – volume: 2 start-page: 21 year: 2018 ident: bib12 article-title: EMG pattern recognition in the era of big data and deep learning publication-title: Big Data Cogn. Comput. – volume: 10 start-page: 9 year: 2016 ident: bib39 article-title: Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands publication-title: Front. Neurorob. – volume: 115 start-page: 211 year: 2015 end-page: 252 ident: bib4 article-title: ImageNet large scale visual recognition challenge publication-title: Int. J. Comput. Vis. – volume: 16 year: 2016 ident: bib36 article-title: Improving the recognition of grips and movements of the hand using myoelectric signals publication-title: BMC Med. Inf. Decis. Making – volume: 8 start-page: 12 year: 2016 ident: bib46 article-title: EMG pattern classification by split and merge deep belief network publication-title: Symmetry – year: 2016 ident: bib63 article-title: TensorFlow: a system for large-scale machine learning publication-title: Proceedings of the USENIX Conference on Operating Systems Design and Implementation – year: 2016 ident: bib1 article-title: Deep Learning – volume: 48 start-page: 643 year: 2011 end-page: 659 ident: bib25 article-title: Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use publication-title: J. Rehabil. Res. Dev. – volume: 2 start-page: 346 year: 2007 end-page: 357 ident: bib17 article-title: Consumer design priorities for upper limb prosthetics publication-title: Disabil. Rehabil. Assist. Technol. – volume: 16 year: 2019 ident: bib49 article-title: Regression convolutional neural network for improved simultaneous EMG control publication-title: J. Neural. Eng. – year: 2018 ident: bib35 article-title: Vector Autoregressive Hierarchical Hidden Markov Models (VARHHMM) for extracting finger movements using multichannel surface EMG signals publication-title: Complexity – year: 2015 ident: bib7 article-title: Going deeper with convolutions publication-title: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. – volume: 40 start-page: 255 year: 2016 end-page: 264 ident: bib20 article-title: Recent advancements in prosthetic hand technology publication-title: J. Med. Eng. Technol. – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: bib2 publication-title: “Deep learning,” – volume: 333 start-page: 838 year: 2011 end-page: 843 ident: bib14 publication-title: “Epidermal electronics,” – volume: 2 start-page: 359 year: 1989 end-page: 366 ident: bib38 article-title: Multilayer feedforward networks are universal approximators publication-title: Neural Network. – volume: 1 start-page: 1135 year: 2015 end-page: 1143 ident: bib56 article-title: Learning both weights and connections for efficient neural network publication-title: In Proceedings of NeurIPS – start-page: 770 year: 2016 end-page: 778 ident: bib8 article-title: Deep residual learning for image recognition publication-title: IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. – ident: bib5 article-title: ImageNet LSVRC – volume: 11 start-page: 379 year: 2017 ident: bib41 article-title: Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on convolutional neural network publication-title: Front. Neurosci. – volume: 70 start-page: 874 year: 2017 end-page: 883 ident: bib51 article-title: AdaNet: adaptive structural learning of artificial neural networks publication-title: Proceedings of the ICML – volume: 18 start-page: 334 year: 2015 end-page: 359 ident: bib24 article-title: Current state of digital signal processing in myoelectric interfaces and related applications publication-title: Biomed. Signal Process Contr. – volume: 119 start-page: 131 year: 2017 end-page: 138 ident: bib42 article-title: A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface publication-title: Pattern Recogn. Lett. – volume: 25 year: 2012 ident: bib6 article-title: ImageNet classification with deep convolutional neural networks publication-title: Proc. Adv. Neural Inf. Process. Syst. – start-page: 5220 year: 2017 end-page: 5224 ident: bib9 article-title: A study on data augmentation of reverberant speech for robust speech recognition publication-title: In Proc. IEEE Int. Conf. Acoust. Speech Signal Process. – year: 2016 ident: bib55 article-title: Neural architecture search with reinforcement learning – start-page: 569 year: 2002 end-page: 577 ident: bib57 article-title: Efficient reinforcement learning through evolving neural network topologies publication-title: Proc. GECCO – volume: 9 year: 2019 ident: bib48 article-title: Extraction of multi-labelled movement information from the raw HD-sEMG image with time-domain depth publication-title: Sci. Rep. – year: 2019 ident: bib66 article-title: Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-On Neural Network Processing” – volume: 32 start-page: 290 year: 2008 end-page: 293 ident: bib23 article-title: Prosthetic hands from touch bionics publication-title: Ind. Robot – year: 2014 ident: bib62 article-title: Adam: a method for stochastic optimization – volume: 39 start-page: 44 year: 2012 end-page: 47 ident: bib32 article-title: Identification of emg signals using discriminant analysis and svm classifier publication-title: Expert Syst. Appl. – year: 2015 ident: bib61 article-title: Batch normalization: accelerating deep network training by reducing internal covariate shift – volume: 19 start-page: 186 year: 2011 end-page: 192 ident: bib65 article-title: Determining the optimal window length for pattern recognition-based myoelectric control publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 15 year: 2016 ident: bib40 article-title: Gesture recognition by instantaneous surface EMG images publication-title: Sci. Rep. – volume: 25 start-page: 1821 year: 2017 end-page: 1831 ident: bib31 article-title: A framework of temporal-spatial descriptors-based feature extraction for improving myoelectric pattern recognition publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 8 start-page: 12 year: 2016 ident: 10.1016/j.compbiomed.2020.103723_bib46 article-title: EMG pattern classification by split and merge deep belief network publication-title: Symmetry doi: 10.3390/sym8120148 – volume: 521 start-page: 436 year: 2015 ident: 10.1016/j.compbiomed.2020.103723_bib2 publication-title: “Deep learning,” Nature – volume: 34 start-page: 397 issue: 4 year: 2012 ident: 10.1016/j.compbiomed.2020.103723_bib15 article-title: Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2011.11.018 – volume: 15 issue: 6 year: 2016 ident: 10.1016/j.compbiomed.2020.103723_bib40 article-title: Gesture recognition by instantaneous surface EMG images publication-title: Sci. Rep. – year: 2015 ident: 10.1016/j.compbiomed.2020.103723_bib7 article-title: Going deeper with convolutions – volume: 16 issue: 2 year: 2016 ident: 10.1016/j.compbiomed.2020.103723_bib36 article-title: Improving the recognition of grips and movements of the hand using myoelectric signals publication-title: BMC Med. Inf. Decis. Making – volume: 20 start-page: 663 issue: 5 year: 2012 ident: 10.1016/j.compbiomed.2020.103723_bib22 article-title: Control of upper limb prostheses: terminology and proportional myoelectric control-a review publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2012.2196711 – volume: 13 year: 2018 ident: 10.1016/j.compbiomed.2020.103723_bib43 article-title: A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition publication-title: PloS One doi: 10.1371/journal.pone.0206049 – volume: 1 start-page: 541 issue: 4 year: 1989 ident: 10.1016/j.compbiomed.2020.103723_bib3 article-title: Backpropagation applied to handwritten zip code recognition publication-title: Neural Comput. doi: 10.1162/neco.1989.1.4.541 – volume: 34 start-page: 419 issue: 3 year: 2011 ident: 10.1016/j.compbiomed.2020.103723_bib28 article-title: Identification of motion from multi-channel EMG signals for control of prosthetic hand publication-title: Australas. Phys. Eng. Sci. Med. doi: 10.1007/s13246-011-0079-z – volume: 8 start-page: 7 year: 2018 ident: 10.1016/j.compbiomed.2020.103723_bib45 article-title: Stacked sparse autoencoders for EMG-based classification of hand motions: a comparative multi day analyses between surface and intramuscular EMG publication-title: Appl. Sci. – ident: 10.1016/j.compbiomed.2020.103723_bib55 – volume: 19 start-page: 186 issue: 2 year: 2011 ident: 10.1016/j.compbiomed.2020.103723_bib65 article-title: Determining the optimal window length for pattern recognition-based myoelectric control publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2010.2100828 – volume: 39 start-page: 7420 issue: 8 year: 2012 ident: 10.1016/j.compbiomed.2020.103723_bib37 article-title: Feature reduction and selection for emg signal classification publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2012.01.102 – volume: 1 start-page: 1135 year: 2015 ident: 10.1016/j.compbiomed.2020.103723_bib56 article-title: Learning both weights and connections for efficient neural network publication-title: In Proceedings of NeurIPS – volume: 25 start-page: 1821 issue: 10 year: 2017 ident: 10.1016/j.compbiomed.2020.103723_bib31 article-title: A framework of temporal-spatial descriptors-based feature extraction for improving myoelectric pattern recognition publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2017.2687520 – volume: 48 start-page: 643 issue: 6 year: 2011 ident: 10.1016/j.compbiomed.2020.103723_bib25 article-title: Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use publication-title: J. Rehabil. Res. Dev. doi: 10.1682/JRRD.2010.09.0177 – volume: 9 issue: 1 year: 2019 ident: 10.1016/j.compbiomed.2020.103723_bib48 article-title: Extraction of multi-labelled movement information from the raw HD-sEMG image with time-domain depth publication-title: Sci. Rep. doi: 10.1038/s41598-019-43676-8 – start-page: 569 year: 2002 ident: 10.1016/j.compbiomed.2020.103723_bib57 article-title: Efficient reinforcement learning through evolving neural network topologies publication-title: Proc. GECCO – volume: 39 start-page: 44 issue: 1 year: 2012 ident: 10.1016/j.compbiomed.2020.103723_bib32 article-title: Identification of emg signals using discriminant analysis and svm classifier publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.06.043 – year: 2018 ident: 10.1016/j.compbiomed.2020.103723_bib35 article-title: Vector Autoregressive Hierarchical Hidden Markov Models (VARHHMM) for extracting finger movements using multichannel surface EMG signals publication-title: Complexity doi: 10.1155/2018/9728264 – year: 2011 ident: 10.1016/j.compbiomed.2020.103723_bib59 article-title: Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm – year: 2016 ident: 10.1016/j.compbiomed.2020.103723_bib44 article-title: Movement intention decoding based on deep learning for multiuser myoelectric interfaces – volume: 8 start-page: 2 year: 1996 ident: 10.1016/j.compbiomed.2020.103723_bib19 article-title: Epidemiologic overview of individuals with upper-limb loss and their reported research priorities publication-title: J. Prosthet. Orthot. doi: 10.1097/00008526-199600810-00003 – volume: 12 start-page: 2493 year: 2010 ident: 10.1016/j.compbiomed.2020.103723_bib10 article-title: Natural language processing (almost) from scratch publication-title: J. Mach. Learn. Res. – volume: 15 start-page: 1929 year: 2014 ident: 10.1016/j.compbiomed.2020.103723_bib60 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. – volume: 2 start-page: 21 issue: 3 year: 2018 ident: 10.1016/j.compbiomed.2020.103723_bib12 article-title: EMG pattern recognition in the era of big data and deep learning publication-title: Big Data Cogn. Comput. doi: 10.3390/bdcc2030021 – ident: 10.1016/j.compbiomed.2020.103723_bib27 – start-page: 770 year: 2016 ident: 10.1016/j.compbiomed.2020.103723_bib8 article-title: Deep residual learning for image recognition publication-title: IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. – volume: 90 start-page: 260 issue: 3 year: 2011 ident: 10.1016/j.compbiomed.2020.103723_bib29 article-title: Online myoelectric control of a dexterous hand prosthesis by transradial amputees publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2011.2108667 – volume: 2 start-page: 346 issue: 6 year: 2007 ident: 10.1016/j.compbiomed.2020.103723_bib17 article-title: Consumer design priorities for upper limb prosthetics publication-title: Disabil. Rehabil. Assist. Technol. doi: 10.1080/17483100701714733 – volume: 22 start-page: 797 issue: 4 year: 2014 ident: 10.1016/j.compbiomed.2020.103723_bib16 article-title: The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2014.2305111 – volume: 18 start-page: 334 year: 2015 ident: 10.1016/j.compbiomed.2020.103723_bib24 article-title: Current state of digital signal processing in myoelectric interfaces and related applications publication-title: Biomed. Signal Process Contr. doi: 10.1016/j.bspc.2015.02.009 – start-page: 293 year: 2018 ident: 10.1016/j.compbiomed.2020.103723_bib53 article-title: Evolving deep neural networks – volume: 11 start-page: 379 year: 2017 ident: 10.1016/j.compbiomed.2020.103723_bib41 article-title: Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on convolutional neural network publication-title: Front. Neurosci. doi: 10.3389/fnins.2017.00379 – volume: 50 start-page: 599 issue: 5 year: 2013 ident: 10.1016/j.compbiomed.2020.103723_bib21 article-title: Mechanical design and performance specifications of anthropomorphic prosthetic hands: a review publication-title: J. Rehabil. Res. Dev. doi: 10.1682/JRRD.2011.10.0188 – volume: 17 start-page: 3 year: 2017 ident: 10.1016/j.compbiomed.2020.103723_bib47 article-title: Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation publication-title: Sensors doi: 10.3390/s17030458 – volume: 2 start-page: 359 issue: 5 year: 1989 ident: 10.1016/j.compbiomed.2020.103723_bib38 article-title: Multilayer feedforward networks are universal approximators publication-title: Neural Network. doi: 10.1016/0893-6080(89)90020-8 – volume: 32 start-page: 290 issue: 4 year: 2008 ident: 10.1016/j.compbiomed.2020.103723_bib23 article-title: Prosthetic hands from touch bionics publication-title: Ind. Robot doi: 10.1108/01439910810876364 – volume: 2 start-page: 2951 year: 2012 ident: 10.1016/j.compbiomed.2020.103723_bib54 article-title: Practical bayesian optimization of machine learning algorithms publication-title: In Proc. Adv. Neural Inf. Process. Syst. – start-page: 11 year: 2017 ident: 10.1016/j.compbiomed.2020.103723_bib34 article-title: Hand gesture recognition research based on surface emg sensors and 2d-accelerometers publication-title: Proc. IEEE Int. Sym. Wrbl. Co. – volume: 30 start-page: 399 issue: 6 year: 2010 ident: 10.1016/j.compbiomed.2020.103723_bib30 article-title: Using EMG for real-time prediction of joint angles of a prosthetic hand equipped with a sensory feedback system publication-title: J. Med. Biol. Eng. doi: 10.5405/jmbe.767 – ident: 10.1016/j.compbiomed.2020.103723_bib61 – year: 2016 ident: 10.1016/j.compbiomed.2020.103723_bib1 – start-page: 5220 year: 2017 ident: 10.1016/j.compbiomed.2020.103723_bib9 article-title: A study on data augmentation of reverberant speech for robust speech recognition publication-title: In Proc. IEEE Int. Conf. Acoust. Speech Signal Process. – volume: 10 start-page: 9 year: 2016 ident: 10.1016/j.compbiomed.2020.103723_bib39 article-title: Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands publication-title: Front. Neurorob. doi: 10.3389/fnbot.2016.00009 – ident: 10.1016/j.compbiomed.2020.103723_bib62 – volume: 1 year: 2014 ident: 10.1016/j.compbiomed.2020.103723_bib64 article-title: Electromyography data for non-invasive naturally-controlled robotic hand prostheses publication-title: Sci. Data doi: 10.1038/sdata.2014.53 – volume: 119 start-page: 131 year: 2017 ident: 10.1016/j.compbiomed.2020.103723_bib42 article-title: A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2017.12.005 – volume: 25 issue: 2 year: 2012 ident: 10.1016/j.compbiomed.2020.103723_bib6 article-title: ImageNet classification with deep convolutional neural networks publication-title: Proc. Adv. Neural Inf. Process. Syst. – year: 2015 ident: 10.1016/j.compbiomed.2020.103723_bib11 – volume: 16 issue: 3 year: 2019 ident: 10.1016/j.compbiomed.2020.103723_bib49 article-title: Regression convolutional neural network for improved simultaneous EMG control publication-title: J. Neural. Eng. doi: 10.1088/1741-2552/ab0e2e – volume: 115 start-page: 211 year: 2015 ident: 10.1016/j.compbiomed.2020.103723_bib4 article-title: ImageNet large scale visual recognition challenge publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-015-0816-y – year: 2016 ident: 10.1016/j.compbiomed.2020.103723_bib33 article-title: EMG pattern recognition using decomposition techniques for constructing multiclass classifiers – volume: 9 start-page: 85 year: 2012 ident: 10.1016/j.compbiomed.2020.103723_bib50 article-title: High-density surface EMG maps from upper-arm and forearm muscles publication-title: J. NeuroEng. Rehabil. doi: 10.1186/1743-0003-9-85 – year: 2016 ident: 10.1016/j.compbiomed.2020.103723_bib63 article-title: TensorFlow: a system for large-scale machine learning – year: 2019 ident: 10.1016/j.compbiomed.2020.103723_bib66 – volume: 70 start-page: 874 year: 2017 ident: 10.1016/j.compbiomed.2020.103723_bib51 article-title: AdaNet: adaptive structural learning of artificial neural networks publication-title: Proceedings of the ICML – volume: 8 start-page: 11 year: 2013 ident: 10.1016/j.compbiomed.2020.103723_bib67 article-title: BioPatRec: a modular research platform for the control of artificial limbs based on pattern recognition algorithms publication-title: Source Code Biol. Med. doi: 10.1186/1751-0473-8-11 – volume: 333 start-page: 838 issue: 6044 year: 2011 ident: 10.1016/j.compbiomed.2020.103723_bib14 publication-title: “Epidermal electronics,” Science – volume: 70 start-page: 2902 year: 2017 ident: 10.1016/j.compbiomed.2020.103723_bib52 article-title: Large-scale evolution of image classifiers publication-title: Proc. ICML – volume: 31 start-page: 362 issue: 4 year: 2007 ident: 10.1016/j.compbiomed.2020.103723_bib18 article-title: Results of an Internet survey of myoelectric prosthetic hand users publication-title: Prosthet. Orthot. Int. doi: 10.1080/03093640601061265 – volume: 76 start-page: 32 issue: 2 year: 2005 ident: 10.1016/j.compbiomed.2020.103723_bib13 article-title: The basics of electromyography publication-title: J. Neurol. Neurosurg. Psychiatry – volume: 40 start-page: 255 issue: 5 year: 2016 ident: 10.1016/j.compbiomed.2020.103723_bib20 article-title: Recent advancements in prosthetic hand technology publication-title: J. Med. Eng. Technol. doi: 10.3109/03091902.2016.1167971 – volume: 30 start-page: 459 issue: 4–6 year: 2002 ident: 10.1016/j.compbiomed.2020.103723_bib26 article-title: Control of multifunctional prosthetic hands by processing the electromyographic signal publication-title: Crit. Rev. Biomed. Eng. doi: 10.1615/CritRevBiomedEng.v30.i456.80 – year: 1996 ident: 10.1016/j.compbiomed.2020.103723_bib58
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Snippet	Convolutional Neural Networks (CNNs) have been subject to extensive attention in the pattern recognition literature due to unprecedented performance in tasks... AbstractConvolutional Neural Networks (CNNs) have been subject to extensive attention in the pattern recognition literature due to unprecedented performance in...
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SubjectTerms	Algorithms Artificial neural networks Bioinformatics (Computational Biology) Bioinformatik (Beräkningsbiologi) Collating Computer and Information Sciences Computer applications Convolutional neural networks Data- och informationsvetenskap (Datateknik) Deep learning Discriminant analysis Electromyography Embedded systems Evolutionary algorithms Information retrieval Interfaces Internal Medicine Iterative methods Machine learning Medical research Model selection Muscle-computer interfaces Muscles Myoelectric control Myoelectric pattern recognition Myoelectricity Natural Sciences Naturvetenskap Network topologies Neural networks Other Pattern recognition Problem solving Prostheses Unstructured data
Title	Automatic discovery of resource-restricted Convolutional Neural Network topologies for myoelectric pattern recognition
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