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

• 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 Science; 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

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...