Surface EMG-based Sketching Recognition Using Two Analysis Windows and Gene Expression Programming

Sketching is one of the most important processes in the conceptual stage of design. Previous studies have relied largely on the analyses of sketching process and outcomes; whereas surface electromyographic (sEMG) signals associated with sketching have received little attention. In this study, we pro...

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Published in:Frontiers in neuroscience Vol. 10; p. 445
Main Authors: Yang, Zhongliang, Chen, Yumiao
Format: Journal Article
Language:English
Published: Switzerland Frontiers Research Foundation 14.10.2016
Frontiers Media S.A
Subjects:
Algorithms
Arm
Classification
Computers
Design
Designers
Electromyography
Forearm
Gene expression
Gene Expression Programming
Image retrieval
Industrial design
Interactive computer systems
Muscle computer interface
Muscles
Neural networks
Neuroscience
Pattern recognition
Principal components analysis
Research methodology
Sketching
Studies
surface electromyography
User interface
User training
China
sketching
gene expression programming
muscle-computer interface
surface electromyography
pattern recognition
ISSN:1662-453X, 1662-4548, 1662-453X
Online Access:Get full text
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Summary:Sketching is one of the most important processes in the conceptual stage of design. Previous studies have relied largely on the analyses of sketching process and outcomes; whereas surface electromyographic (sEMG) signals associated with sketching have received little attention. In this study, we propose a method in which 11 basic one-stroke sketching shapes are identified from the sEMG signals generated by the forearm and upper arm muscles from 4 subjects. Time domain features such as integrated electromyography, root mean square and mean absolute value were extracted with analysis windows of two length conditions for pattern recognition. After reducing data dimensionality using principal component analysis, the shapes were classified using Gene Expression Programming (GEP). The performance of the GEP classifier was compared to the Back Propagation neural network (BPNN) and the Elman neural network (ENN). Feature extraction with the short analysis window (250 ms with a 250 ms increment) improved the recognition rate by around 6.4% averagely compared with the long analysis window (2500 ms with a 2500 ms increment). The average recognition rate for the eleven basic one-stroke sketching patterns achieved by the GEP classifier was 96.26% in the training set and 95.62% in the test set, which was superior to the performance of the BPNN and ENN classifiers. The results show that the GEP classifier is able to perform well with either length of the analysis window. Thus, the proposed GEP model show promise for recognizing sketching based on sEMG signals.
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This article was submitted to Neural Technology, a section of the journal Frontiers in Neuroscience
Edited by: Ning Jiang, University of Waterloo, Canada
Reviewed by: Giuseppe D'Avenio, Istituto Superiore di Sanità, Italy; Alexei Ossadtchi, St. Petersburg State University, Russia
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2016.00445