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Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees

Cipriani, Christian; Antfolk, Christian LU ; Controzzi, Marco; Lundborg, Göran LU ; Rosén, Birgitta LU ; Carrozza, Maria Chiara and Sebelius, Fredrik LU (2011) In IEEE Transactions on Neural Systems and Rehabilitation Engineering 19(3). p.260-270
Abstract
A real-time pattern recognition algorithm based on k-nearest neighbors and lazy learning was used to classify, voluntary electromyography (EMG) signals and to simultaneously control movements of a dexterous artificial hand. EMG signals were superficially recorded by eight pairs of electrodes from the stumps of five transradial amputees and forearms of five able-bodied participants and used online to control a robot hand. Seven finger movements (not involving the wrist) were investigated in this study. The first objective was to understand whether and to which extent it is possible to control continuously and in real-time, the finger postures of a prosthetic hand, using superficial EMG, and a practical classifier, also taking advantage of... (More)
A real-time pattern recognition algorithm based on k-nearest neighbors and lazy learning was used to classify, voluntary electromyography (EMG) signals and to simultaneously control movements of a dexterous artificial hand. EMG signals were superficially recorded by eight pairs of electrodes from the stumps of five transradial amputees and forearms of five able-bodied participants and used online to control a robot hand. Seven finger movements (not involving the wrist) were investigated in this study. The first objective was to understand whether and to which extent it is possible to control continuously and in real-time, the finger postures of a prosthetic hand, using superficial EMG, and a practical classifier, also taking advantage of the direct visual feedback of the moving hand. The second objective was to calculate statistical differences in the performance between participants and groups, thereby assessing the general applicability of the proposed method. The average accuracy of the classifier was 79% for amputees and 89% for able-bodied participants. Statistical analysis of the data revealed a difference in control accuracy based on the aetiology of amputation, type of prostheses regularly used and also between able-bodied participants and amputees. These results are encouraging for the development of noninvasive EMG interfaces for the control of dexterous prostheses. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Dexterous prosthesis, electromyography (EMG), pattern recognition, real-time control, transradial amputation
in
IEEE Transactions on Neural Systems and Rehabilitation Engineering
volume
19
issue
3
pages
260 - 270
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000291403500005
  • scopus:79958727631
ISSN
1534-4320
DOI
10.1109/TNSRE.2011.2108667
language
English
LU publication?
yes
id
22940bc8-ab85-440d-9c7f-9be52f2a393c (old id 1984876)
date added to LUP
2011-07-01 09:11:03
date last changed
2017-11-05 04:14:28
@article{22940bc8-ab85-440d-9c7f-9be52f2a393c,
  abstract     = {A real-time pattern recognition algorithm based on k-nearest neighbors and lazy learning was used to classify, voluntary electromyography (EMG) signals and to simultaneously control movements of a dexterous artificial hand. EMG signals were superficially recorded by eight pairs of electrodes from the stumps of five transradial amputees and forearms of five able-bodied participants and used online to control a robot hand. Seven finger movements (not involving the wrist) were investigated in this study. The first objective was to understand whether and to which extent it is possible to control continuously and in real-time, the finger postures of a prosthetic hand, using superficial EMG, and a practical classifier, also taking advantage of the direct visual feedback of the moving hand. The second objective was to calculate statistical differences in the performance between participants and groups, thereby assessing the general applicability of the proposed method. The average accuracy of the classifier was 79% for amputees and 89% for able-bodied participants. Statistical analysis of the data revealed a difference in control accuracy based on the aetiology of amputation, type of prostheses regularly used and also between able-bodied participants and amputees. These results are encouraging for the development of noninvasive EMG interfaces for the control of dexterous prostheses.},
  author       = {Cipriani, Christian and Antfolk, Christian and Controzzi, Marco and Lundborg, Göran and Rosén, Birgitta and Carrozza, Maria Chiara and Sebelius, Fredrik},
  issn         = {1534-4320},
  keyword      = {Dexterous prosthesis,electromyography (EMG),pattern recognition,real-time control,transradial amputation},
  language     = {eng},
  number       = {3},
  pages        = {260--270},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  title        = {Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees},
  url          = {http://dx.doi.org/10.1109/TNSRE.2011.2108667},
  volume       = {19},
  year         = {2011},
}