Using EMG for Real-time Prediction of Joint Angles to Control a Prosthetic Hand Equipped with a Sensory Feedback System
Antfolk, Christian LU ; Cipriani, Christian ; Controzzi, Marco ; Carrozza, Maria Chiara ; Lundborg, Göran LU ; Rosén, Birgitta LU and Sebelius, Fredrik LU
(2010)
In Journal of Medical and Biological Engineering
30(6).
p.399-405
- Abstract
- All commercially available upper limb prosthesis controllers only allow the hand to be commanded in an open and close fashion without any sensory feedback to the user. Here the evaluation of a multi-degree of freedom hand controlled using a real-time EMG pattern recognition algorithm and incorporating a sensory feedback system is reported. The hand prosthesis, called SmartHand, was controlled in real-time by using 16 myoelectric signals from the residual limb of a 25-year old male transradial amputee in a two day long evaluation session. Initial training of the EMG pattern recognition algorithm was performed with a dataglove fitted to the contralateral hand recording joint angle positions of the fingers and mapping joint angles of the... (More)
- All commercially available upper limb prosthesis controllers only allow the hand to be commanded in an open and close fashion without any sensory feedback to the user. Here the evaluation of a multi-degree of freedom hand controlled using a real-time EMG pattern recognition algorithm and incorporating a sensory feedback system is reported. The hand prosthesis, called SmartHand, was controlled in real-time by using 16 myoelectric signals from the residual limb of a 25-year old male transradial amputee in a two day long evaluation session. Initial training of the EMG pattern recognition algorithm was performed with a dataglove fitted to the contralateral hand recording joint angle positions of the fingers and mapping joint angles of the fingers to the EMG data. In the following evaluation sessions, the myoelectric signals were classified using local approximation and lazy learning, producing finger joint angle outputs and consequently controlling the prosthetic hand. Sensory information recorded from force sensors in the artificial hand was relayed to actuators, integrated in the socket of the prosthesis, continuously delivering force sensory feedback stimulations to the stump of the amputee. The participant was able to perform several dextrous movements as well as functional grip tasks after only two hours of training and increased his controllability during the two day session. In the final evaluation session a mean classification accuracy of 86% was achieved. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1814798
- author
- Antfolk, Christian
LU
; Cipriani, Christian
; Controzzi, Marco
; Carrozza, Maria Chiara
; Lundborg, Göran
LU
; Rosén, Birgitta
LU
and Sebelius, Fredrik
LU
- organization
- publishing date
- 2010
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Prosthetic hand, EMG signal acquisition, Myoelectric control
- in
- Journal of Medical and Biological Engineering
- volume
- 30
- issue
- 6
- pages
- 399 - 405
- publisher
- Springer Science and Business Media B.V.
- external identifiers
-
- wos:000285723100008
- scopus:78751498290
- ISSN
- 1609-0985
- DOI
- 10.5405/jmbe.767
- language
- English
- LU publication?
- yes
- id
- e2a5692c-4d87-4004-8f06-dbea4ed9b753 (old id 1814798)
- date added to LUP
- 2016-04-01 13:40:23
- date last changed
- 2025-04-04 13:59:34
@article{e2a5692c-4d87-4004-8f06-dbea4ed9b753,
abstract = {{All commercially available upper limb prosthesis controllers only allow the hand to be commanded in an open and close fashion without any sensory feedback to the user. Here the evaluation of a multi-degree of freedom hand controlled using a real-time EMG pattern recognition algorithm and incorporating a sensory feedback system is reported. The hand prosthesis, called SmartHand, was controlled in real-time by using 16 myoelectric signals from the residual limb of a 25-year old male transradial amputee in a two day long evaluation session. Initial training of the EMG pattern recognition algorithm was performed with a dataglove fitted to the contralateral hand recording joint angle positions of the fingers and mapping joint angles of the fingers to the EMG data. In the following evaluation sessions, the myoelectric signals were classified using local approximation and lazy learning, producing finger joint angle outputs and consequently controlling the prosthetic hand. Sensory information recorded from force sensors in the artificial hand was relayed to actuators, integrated in the socket of the prosthesis, continuously delivering force sensory feedback stimulations to the stump of the amputee. The participant was able to perform several dextrous movements as well as functional grip tasks after only two hours of training and increased his controllability during the two day session. In the final evaluation session a mean classification accuracy of 86% was achieved.}},
author = {{Antfolk, Christian and Cipriani, Christian and Controzzi, Marco and Carrozza, Maria Chiara and Lundborg, Göran and Rosén, Birgitta and Sebelius, Fredrik}},
issn = {{1609-0985}},
keywords = {{Prosthetic hand; EMG signal acquisition; Myoelectric control}},
language = {{eng}},
number = {{6}},
pages = {{399--405}},
publisher = {{Springer Science and Business Media B.V.}},
series = {{Journal of Medical and Biological Engineering}},
title = {{Using EMG for Real-time Prediction of Joint Angles to Control a Prosthetic Hand Equipped with a Sensory Feedback System}},
url = {{http://dx.doi.org/10.5405/jmbe.767}},
doi = {{10.5405/jmbe.767}},
volume = {{30}},
year = {{2010}},
}