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Concept of a system for training of bioprosthetic hand control in one side handless humans using virtual reality and visual and sensory biofeedback

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the paper the concept of a training system is presented which can help to stimulate sensory-motor cortex centers in order to develop their ability for efficient use of bioprosthesis. The basis of the training system is a virtual reality with a virtual hand, that the trained patient can move and concurrently observe the movement on the screen (visual feedback) and whose contact with virtual objects the patient may feel as a touch (sensory feedback). The construction of the virtual hand consists of physical elements, connected by joints, a graphical object representing the structure of the hand and the bones enable its deformation. The control procedure of virtual hand is realized through recognition of intention of hand motion on the basis of EMG signals coming from the stump muscles. The recognition algorithm is constructed using the learning set, i.e the set of pairs containing the class of hand fingers movement and accompanying myopotentials segments, which are acquired from the muscles of healthy hand.
Rocznik
Tom
Strony
85--91
Opis fizyczny
Bibliogr. 15 poz., rys.
Twórcy
  • Wroclaw University of Technology, Faculty of Electronics
autor
Bibliografia
  • [1] AJIBOYE A., WEIR F., Muscle synergies as a predictive framework for the EMG patterns of new hand postures, Journal of Neural Engineering, Vol. 6, 2009.
  • [2] DE LUCA C., Electromyography, In: Encyclopedia of Medical Devices and Instrumentation, John Wiley Publisher, 2006, pp. 98-109.
  • [3] KURZYNSKI M., Pattern recognition – statistical approach, Wroclaw University of Technology Publishers, Wroclaw, 1997, (in Polish).
  • [4] KURZYNSKI M., WOLCZOWSKI A., Neural networks with AR model coefficients applied to the EMG signal classification, Proc. 6th Int. Workshop on Artificial Neural Network and Intelligent Information Processing, ANNIIP, Funchal, 2010, pp. 63–70.
  • [5] KURZYNSKI M., WOLCZOWSKI A., Dynamic selection of classifier ensemble applied to the recognition of EMG signal for the control of bioprosthetic hand, Proc. 11th Int. Conf. on Control, Automation and Systems, Seoul, 2011 (in press).
  • [6] KLEIN M., BRETELER K., et al., Timing of muscle activation in a hand movement sequence, Cerebral Cortex, Vol. 17, 2007, pp. 803-815.
  • [7] MAIER S., VAN DER SMAGT P., Surface EMG suffices to classify the motion of each finger independently, Proceedings of MOVIC 2008, 9th International Conference on Motion and Vibration Control, 2008.
  • [8] MANLY T., ROBERTSON I., KAPUR N., Paradoxes in neurorehabilitation, In: KAPUR N. (ed.) The Paradoxical Brain, Cambridge University Press, 2011, pp. 74-93.
  • [9] RAMACHANDRAN V., ALTSCHULER E., The use of visual feedback in particular mirror visual feedback in restoring brain function, Brain 132, 2009, pp. 693-710.
  • [10] RAMACHANDRAN V., HIRSTEIN W., The paradoxical self, In: KAPUR N. (ed.) The Paradoxical Brain, Cambridge University Press, 2011, pp. 94-109.
  • [11] WOJTCZAK P, WOLCZOWSKI A., KURZYNSKI M., et al., Hand movement recognition based on biosignal analysis, Engineering Applications of Artificial Intelligence, Vol. 22, 2009, pp. 608-615.
  • [12] WOLCZOWSKI A., KURZYNSKI M., Human–machine interface in bio-prosthesis control using EMG signal classification, Expert Systems, Vol. 27, No. 1, 2010, pp. 53-70.
  • [13] WOLCZOWSKI A., KURZYNSKI M., Control of artificial hand via recognition of EMG Signals, LNCS 3337, Springer Verlag, 2004, pp. 356-367.
  • [14] WOLCZOWSKI A., Smart hand: The concept of sensor based control, Proc. of 7th IEEE[Int. Symposium MMAR, 2001, pp. 783-790.
  • [15] WOLOSZYNSKI T., KURZYNSKI M., A probabilistic model of classifier competence for dynamic ensemble selection, Pattern Recognition, Vol. 44, 2011, pp. 2656-2668.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0025-0009
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