Myoelectric control of a computer animated hand: A new concept based on the combined use of a tree-structured artificial neural network and a data glove
This paper proposes a new learning set-up in the field of control systems for multifunctional hand prostheses. Two male subjects with a traumatic one-hand amputation performed simultaneous symmetric movements with the healthy and the phantom hand. A data glove on the healthy hand was used as a refer...
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| Published in | Journal of medical engineering & technology Vol. 30; no. 1; pp. 2 - 10 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Informa UK Ltd
2006
Taylor & Francis |
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| Abstract | This paper proposes a new learning set-up in the field of control systems for multifunctional hand prostheses. Two male subjects with a traumatic one-hand amputation performed simultaneous symmetric movements with the healthy and the phantom hand. A data glove on the healthy hand was used as a reference to train the system to perform natural movements. Instead of a physical prosthesis with limited degrees of freedom, a virtual (computer-animated) hand was used as the target tool. Both subjects successfully performed seven different motoric actions with the fingers and wrist. To reduce the training time for the system, a tree-structured, self-organizing, artificial neural network was designed. The training time never exceeded 30 seconds for any of the configurations used, which is three to four times faster than most currently used artificial neural network (ANN) architectures. |
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| AbstractList | This paper proposes a new learning set-up in the field of control systems for multifunctional hand prostheses. Two male subjects with a traumatic one-hand amputation performed simultaneous symmetric movements with the healthy and the phantom hand. A data glove on the healthy hand was used as a reference to train the system to perform natural movements. Instead of a physical prosthesis with limited degrees of freedom, a virtual (computer-animated) hand was used as the target tool. Both subjects successfully performed seven different motoric actions with the fingers and wrist. To reduce the training time for the system, a tree-structured, self-organizing, artificial neural network was designed. The training time never exceeded 30 seconds for any of the configurations used, which is three to four times faster than most currently used artificial neural network (ANN) architectures.This paper proposes a new learning set-up in the field of control systems for multifunctional hand prostheses. Two male subjects with a traumatic one-hand amputation performed simultaneous symmetric movements with the healthy and the phantom hand. A data glove on the healthy hand was used as a reference to train the system to perform natural movements. Instead of a physical prosthesis with limited degrees of freedom, a virtual (computer-animated) hand was used as the target tool. Both subjects successfully performed seven different motoric actions with the fingers and wrist. To reduce the training time for the system, a tree-structured, self-organizing, artificial neural network was designed. The training time never exceeded 30 seconds for any of the configurations used, which is three to four times faster than most currently used artificial neural network (ANN) architectures. This paper proposes a new learning set-up in the field of control systems for multifunctional hand prostheses. Two male subjects with a traumatic one-hand amputation performed simultaneous symmetric movements with the healthy and the phantom hand. A data glove on the healthy hand was used as a reference to train the system to perform natural movements. Instead of a physical prosthesis with limited degrees of freedom, a virtual (computer-animated) hand was used as the target tool. Both subjects successfully performed seven different motoric actions with the fingers and wrist. To reduce the training time for the system, a tree-structured, self-organizing, artificial neural network was designed. The training time never exceeded 30 seconds for any of the configurations used, which is three to four times faster than most currently used artificial neural network (ANN) architectures. |
| Author | Sebelius, F. Balkenius, C. Laurell, T. Eriksson, L. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16393847$$D View this record in MEDLINE/PubMed https://lup.lub.lu.se/record/630027$$DView record from Swedish Publication Index oai:portal.research.lu.se:publications/1ecaa99a-42da-4eae-84c8-4d5a39938fe4$$DView record from Swedish Publication Index |
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| SubjectTerms | Amputees - rehabilitation Computer and Information Science Computer and Information Sciences Computer Graphics Computer graphics and computer vision Computer Peripherals Computer Vision and Robotics (Autonomous Systems) Computer-Assisted Instruction - methods Data- och informationsvetenskap (Datateknik) Datorgrafik och datorseende Datorseende och robotik (autonoma system) Decision Support Techniques Electromyography - methods Feedback Hand - innervation Hand - physiopathology Humans Movement Natural Sciences Naturvetenskap Neural Networks (Computer) Prosthesis Fitting - methods Therapy, Computer-Assisted - methods User-Computer Interface |
| Title | Myoelectric control of a computer animated hand: A new concept based on the combined use of a tree-structured artificial neural network and a data glove |
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