loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Iker Mesa 1 ; Javier Diaz 2 ; Angel Rubio 2 ; Beatriz Sedano 2 and Jon Legarda 1

Affiliations: 1 CEIT - Centro de Estudios e Investigaciones Técnicas de Gipuzkoa, Spain ; 2 CEIT, Spain

ISBN: 978-989-8425-89-8

Keyword(s): EMG, sEMG, mRMR, SVM, Pattern recognition, Variable selection, Feature selection.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Cybernetics and User Interface Technologies ; Data Manipulation ; Devices ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Information and Systems Security ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: In this work 32 surface Electromyography (sEMG) electrode locations and 41 signal-features are evaluated in order to achieve an accurate classification rate in a static-hand gesture classification task. A novel implementation of the minimal Redundancy Maximal Relevance (mRMR) Variable Selection algorithm is proposed with the aim of selecting the most informative and least redundant combination of sEMG channels and signal features. The performance of the new algorithm and of the selected set of channels and signal-features are tested with a Support Vector Machine classifier.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.173.57.202

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Mesa, I.; Rubio, A.; Diaz, J.; Legarda, J. and Sedano, B. (2012). REDUCING THE NUMBER OF CHANNELS AND SIGNAL-FEATURES FOR AN ACCURATE CLASSIFICATION IN AN EMG PATTERN RECOGNITION TASK.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012) ISBN 978-989-8425-89-8, pages 38-48. DOI: 10.5220/0003767300380048

@conference{biosignals12,
author={Iker Mesa. and Angel Rubio. and Javier Diaz. and Jon Legarda. and Beatriz Sedano.},
title={REDUCING THE NUMBER OF CHANNELS AND SIGNAL-FEATURES FOR AN ACCURATE CLASSIFICATION IN AN EMG PATTERN RECOGNITION TASK},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)},
year={2012},
pages={38-48},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003767300380048},
isbn={978-989-8425-89-8},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)
TI - REDUCING THE NUMBER OF CHANNELS AND SIGNAL-FEATURES FOR AN ACCURATE CLASSIFICATION IN AN EMG PATTERN RECOGNITION TASK
SN - 978-989-8425-89-8
AU - Mesa, I.
AU - Rubio, A.
AU - Diaz, J.
AU - Legarda, J.
AU - Sedano, B.
PY - 2012
SP - 38
EP - 48
DO - 10.5220/0003767300380048

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.