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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Bibliographic Details
Published inJournal of medical engineering & technology Vol. 30; no. 1; pp. 2 - 10
Main Authors Sebelius, F., Eriksson, L., Balkenius, C., Laurell, T.
Format Journal Article
LanguageEnglish
Published England Informa UK Ltd 2006
Taylor & Francis
Subjects
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
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Summary: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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ISSN:0309-1902
1464-522X
1464-522X
DOI:10.1080/03091900512331332546