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SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics


Authors
Elena Ceseracciu, Dept. of Inf. Eng., Univ. of Padova, Padova, Italy
Monica Reggiani
Zimi Sawacha, Dept. of Inf. Eng., Univ. of Padova, Padova, Italy
Massimo Sartori, Dept. of Inf. Eng., Univ. of Padova, Padova, Italy
Fabiana Spolaor, Dept. of Inf. Eng., Univ. of Padova, Padova, Italy
Claudio Cobelli, Dept. of Inf. Eng., Univ. of Padova, Padova, Italy
Enrico Pagello, Dept. of Inf. Eng., Univ. of Padova, Padova, Italy

Published in
The 18th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).
pages 165 -170, Viareggio (Italy), September 2010
doi>10.1109/ROMAN.2010.5598664

Abstract
The next generation of tools for rehabilitation robotics requires advanced human-robot interfaces able to activate the device as soon as patient's motion intention is raised. This paper investigated the suitability of Support Vector Machine (SVM) classifiers for identification of locomotion intentions from surface electromyography (sEMG) data. A phase-dependent approach, based on foot contact and foot push off events, was employed in order to contextualize muscle activation signals. Good accuracy is demonstrated on experimental data from three healthy subjects. Classification has also been tested for different subsets of EMG features and muscles, aiming to identify a minimal setup required for the control of an EMG-based exoskeleton for rehabilitation purposes.

Bibtex
@inproceedings{ROMAN2010,
     Author={Ceseracciu, E. and Reggiani, M. and Sawacha, Z. and Sartori, M. and Spolaor, F. and Cobelli, C. and Pagello, E.},
     Title={{SVM} classification of locomotion modes using surface electromyography for applications in rehabilitation robotics},
     Booktitle={Robot and Human Interactive Communication (RO-MAN). The 18th IEEE International Symposium on},
     Month=sep,
     Year={2010},
     Pages={165 -170},
     Issn={1944-9445},
     Doi={10.1109/ROMAN.2010.5598664}}

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