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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Vydáno v:Computers in biology and medicine Ročník 120; s. 103723
Hlavní autoři: Olsson, Alexander E., Björkman, Anders, Antfolk, Christian
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.05.2020
Elsevier Limited
Témata:
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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Shrnutí: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.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2020.103723