Refined myoelectric control in below-elbow amputees using artificial neural networks and data glove
Sebelius, Fredrik LU
; Rosén, Birgitta
LU
and Lundborg, Göran
LU
(2005)
In The Journal of Hand Surgery
30(4).
p.780-789
- Abstract
- Purpose: To develop a system for refined motor control of artificial hands based on multiple electromyographic (EMG) recordings, allowing multiple patterns of hand movements. Methods: Five subjects with traumatic below-elbow amputations and 1 subject with a congenital below-elbow failure of formation performed 10 imaginary movements with their phantom hand while surface electrodes recorded the EMG data. In a training phase a data glove with 18 degrees of freedom was used for positional recording of movements in the contralateral healthy hand. These movements were performed at the same time as the imaginary movements in the phantom hand. An artificial neural network (ANN) then could be trained to associate the specific EMG patterns recorded... (More)
- Purpose: To develop a system for refined motor control of artificial hands based on multiple electromyographic (EMG) recordings, allowing multiple patterns of hand movements. Methods: Five subjects with traumatic below-elbow amputations and 1 subject with a congenital below-elbow failure of formation performed 10 imaginary movements with their phantom hand while surface electrodes recorded the EMG data. In a training phase a data glove with 18 degrees of freedom was used for positional recording of movements in the contralateral healthy hand. These movements were performed at the same time as the imaginary movements in the phantom hand. An artificial neural network (ANN) then could be trained to associate the specific EMG patterns recorded from the amputation stump with the analogous specific hand movements synchronously performed in the healthy hand. The ability of the ANN to predict the 10 imaginary movements off line, when they were reflected in a virtual computer hand, was assessed and calculated. Results: After the ANN was trained the subjects were able to perform and control 10 hand movements in the virtual computer hand. The subjects showed a median performance of 5 types of movement with a high correlation with the movement pattern of the data glove. The subjects seemed to relearn to execute motor commands rapidly that had been learned before the accident, independent of how old the injury was. The subject with congenital below-elbow failure of formation was able to perform and control several hand movements in the computer hand that cannot be performed in a myoelectric prosthesis (eg, opposition of the thumb). Conclusions: With the combined use of an ANN and a data glove, acting in concert in a training phase, amputees rapidly can learn to execute several imaginary movements in a virtual computerized hand, this opens promising possibilities for motor control of future hand prostheses. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/231509
- author
- Sebelius, Fredrik
LU
; Rosén, Birgitta
LU
and Lundborg, Göran
LU
- organization
- publishing date
- 2005
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- EMG recordings, hand prosthesis, ANN, control, myoelectric, virtual hands, amputee
- in
- The Journal of Hand Surgery
- volume
- 30
- issue
- 4
- pages
- 780 - 789
- publisher
- Elsevier
- external identifiers
-
- pmid:16039372
- wos:000230804700020
- scopus:22644440851
- ISSN
- 1531-6564
- DOI
- 10.1016/j.jhsa.2005.01.002
- language
- English
- LU publication?
- yes
- id
- 03a852e6-f2a6-4cfc-aae8-4215db0c349a (old id 231509)
- date added to LUP
- 2016-04-01 16:47:20
- date last changed
- 2022-02-05 18:37:14
@article{03a852e6-f2a6-4cfc-aae8-4215db0c349a,
abstract = {{Purpose: To develop a system for refined motor control of artificial hands based on multiple electromyographic (EMG) recordings, allowing multiple patterns of hand movements. Methods: Five subjects with traumatic below-elbow amputations and 1 subject with a congenital below-elbow failure of formation performed 10 imaginary movements with their phantom hand while surface electrodes recorded the EMG data. In a training phase a data glove with 18 degrees of freedom was used for positional recording of movements in the contralateral healthy hand. These movements were performed at the same time as the imaginary movements in the phantom hand. An artificial neural network (ANN) then could be trained to associate the specific EMG patterns recorded from the amputation stump with the analogous specific hand movements synchronously performed in the healthy hand. The ability of the ANN to predict the 10 imaginary movements off line, when they were reflected in a virtual computer hand, was assessed and calculated. Results: After the ANN was trained the subjects were able to perform and control 10 hand movements in the virtual computer hand. The subjects showed a median performance of 5 types of movement with a high correlation with the movement pattern of the data glove. The subjects seemed to relearn to execute motor commands rapidly that had been learned before the accident, independent of how old the injury was. The subject with congenital below-elbow failure of formation was able to perform and control several hand movements in the computer hand that cannot be performed in a myoelectric prosthesis (eg, opposition of the thumb). Conclusions: With the combined use of an ANN and a data glove, acting in concert in a training phase, amputees rapidly can learn to execute several imaginary movements in a virtual computerized hand, this opens promising possibilities for motor control of future hand prostheses.}},
author = {{Sebelius, Fredrik and Rosén, Birgitta and Lundborg, Göran}},
issn = {{1531-6564}},
keywords = {{EMG recordings; hand prosthesis; ANN; control; myoelectric; virtual hands; amputee}},
language = {{eng}},
number = {{4}},
pages = {{780--789}},
publisher = {{Elsevier}},
series = {{The Journal of Hand Surgery}},
title = {{Refined myoelectric control in below-elbow amputees using artificial neural networks and data glove}},
url = {{http://dx.doi.org/10.1016/j.jhsa.2005.01.002}},
doi = {{10.1016/j.jhsa.2005.01.002}},
volume = {{30}},
year = {{2005}},
}