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
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.