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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Veröffentlicht in:	Computers in biology and medicine Jg. 120; S. 103723
Hauptverfasser:	Olsson, Alexander E., Björkman, Anders, Antfolk, Christian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.05.2020 Elsevier Limited
Schlagworte:	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. 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. 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
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32421642$$D View this record in MEDLINE/PubMed
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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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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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