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Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm.

Kanitz, Gunter; Antfolk, Christian LU ; Cipriani, Christian; Sebelius, Fredrik LU and Carrozza, Maria Chiara (2011) 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society In [Host publication title missing] p.1608-1611
Abstract
In this paper we present surface electromyographic (EMG) data collected from 16 channels on five unimpaired subjects and one transradial amputee performing 12 individual finger movements and a rest class. EMG were processed using a traditional Time Domain feature-set and classifiers: a Linear Discriminant Analysis (LDA) a k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Using continuous datasets we show that it is possible to achieve an accuracy up to 80% across subjects. Thereafter possibilities to reduce the numbers of channels physically required, as well as the number of features have been investigated by means of a developed Genetic Algorithm (GA) that included a bonus system to reward eliminated features and channels. The... (More)
In this paper we present surface electromyographic (EMG) data collected from 16 channels on five unimpaired subjects and one transradial amputee performing 12 individual finger movements and a rest class. EMG were processed using a traditional Time Domain feature-set and classifiers: a Linear Discriminant Analysis (LDA) a k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Using continuous datasets we show that it is possible to achieve an accuracy up to 80% across subjects. Thereafter possibilities to reduce the numbers of channels physically required, as well as the number of features have been investigated by means of a developed Genetic Algorithm (GA) that included a bonus system to reward eliminated features and channels. The classification was performed firstly on the full datasets and in later runs using the GA. The GA demonstrated high redundancy in the recorded 16 channel data as well as the insignificance of certain features. Although the GA optimization yielded to reduce 8 to 11 channels depending on the subject, such reduction had little to no effect on the classification accuracies. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
[Host publication title missing]
pages
1608 - 1611
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
external identifiers
  • scopus:84055199923
ISSN
1557-170X
ISBN
978-1-4244-4121-1
DOI
10.1109/IEMBS.2011.6090465
language
English
LU publication?
yes
id
4254fb2c-2273-4a07-9dec-7108af8014ed (old id 4646257)
date added to LUP
2014-09-15 10:07:59
date last changed
2017-11-05 03:56:48
@inproceedings{4254fb2c-2273-4a07-9dec-7108af8014ed,
  abstract     = {In this paper we present surface electromyographic (EMG) data collected from 16 channels on five unimpaired subjects and one transradial amputee performing 12 individual finger movements and a rest class. EMG were processed using a traditional Time Domain feature-set and classifiers: a Linear Discriminant Analysis (LDA) a k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Using continuous datasets we show that it is possible to achieve an accuracy up to 80% across subjects. Thereafter possibilities to reduce the numbers of channels physically required, as well as the number of features have been investigated by means of a developed Genetic Algorithm (GA) that included a bonus system to reward eliminated features and channels. The classification was performed firstly on the full datasets and in later runs using the GA. The GA demonstrated high redundancy in the recorded 16 channel data as well as the insignificance of certain features. Although the GA optimization yielded to reduce 8 to 11 channels depending on the subject, such reduction had little to no effect on the classification accuracies.},
  author       = {Kanitz, Gunter and Antfolk, Christian and Cipriani, Christian and Sebelius, Fredrik and Carrozza, Maria Chiara},
  booktitle    = {[Host publication title missing]},
  isbn         = {978-1-4244-4121-1},
  issn         = {1557-170X},
  language     = {eng},
  pages        = {1608--1611},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm.},
  url          = {http://dx.doi.org/10.1109/IEMBS.2011.6090465},
  year         = {2011},
}