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Design of a myoelectric glove for upper limb stroke rehabilitation

Published: 22 April 2009 Publication History

Abstract

Physiotherapy is an inherent component of stroke rehabilitation. It is important for the patient to be self motivated in physiotherapy sessions for a better and faster recovery. This paper presents initial work on simple design of an orthotic glove which will be controlled by the myoelectric signals of the stroke patient. A real time control scheme using a linear discriminant classifier is used to process and classify the myoelectric signals acquired from different muscle groups. These control signals are then used to actuate servo motors to facilitate the elbow movement. A position and velocity sensor ensures that there are no sudden movements or jerks in the movement path of the orthotic glove.

References

[1]
World Health Report -- 2007, World Health Organization.
[2]
Kalra, L, "The influence of stroke unit rehabilitation on functional recovery of stroke," Stroke 1994, vol 25, pp. 821--825
[3]
Ferraro, M., Palazzolo, J. J., Krol, J., Krebs, H. I., Hogan, N. and Volpe, B., "Robot-aided sensorimotor arm training improves outcome in patients with chronic stroke," Neurology, vol. 61(11), 9 December 2003, pp 1604--1607.
[4]
Stein, Joel, Narendran, Kailas McBean, John, Krebs, Kathryn and Hughes, Richard, "Electromyography-Controlled Exoskeletal Upper-Limb-Powered Orthosis for Exercise Training After Stroke," American Journal of Physical Medicine & Rehabilitation:Volume 86(4)April 2007 pp 255--261.
[5]
Marcello Mulas, Michele Folgheraiter and Giuseppina Gini. "An EMG-controlled Exoskeleton for Hand Rehabilitation". Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics, June 28--July 1, 2005, Chicago, IL, USA.
[6]
H. Krebs, D. Williams, and N. Hogan, "Robot for wrist rehabilitation", Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, October 2001.
[7]
Bernard Hudgins, Philip Parker and Robert N. Scott. "A new strategy for multifunction myoelectric control," IEEE Trans. Biomed. Eng., vol. 40, no. 1, Jan 1993, pp. 82--94.
[8]
R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification. New York: Wiley 2001.
[9]
K Englehart and B. Hudgins, "A robust, real-time control scheme for multifunction myoelectric control," IEEE Trans. Biomed. Eng., vol. 50, no. 7, pp. 848--854, Jul. 2003
[10]
K Englehart, B. Hudgins and Parkar, P. A., "A wavelet-based continuous classification scheme for multifunction myoelectric control," IEEE Trans. Biomed. Eng., vol. 48, no. 3, pp. 302--311, Mar. 2001.
[11]
Surface Electromyography: Detection and Recording. Delsys Inc., Boston, MA, 2002
[12]
Volpe, B. T., Krebs, H., Hogan, N., Edelstein, L., Diels, C. and Aisen, M. A novel approach to stroke rehabilitation: Robot-aided sensorimotor stimulation. Neurology, vol. 54(10), 23 May 2000, pp 1938--1944.

Cited By

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  • (2015)Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer PerceptronProcedia Computer Science10.1016/j.procs.2015.12.34676(223-228)Online publication date: 2015

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cover image ACM Other conferences
i-CREATe '09: Proceedings of the 3rd International Convention on Rehabilitation Engineering & Assistive Technology
April 2009
222 pages
ISBN:9781605587929
DOI:10.1145/1592700
  • Conference Chairs:
  • Wei Tech Ang,
  • Wantanee Phantachat
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 22 April 2009

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Author Tags

  1. EMG
  2. dynamic physiotherapy
  3. linear discriminant classifier
  4. myoelectric control

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  • (2015)Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer PerceptronProcedia Computer Science10.1016/j.procs.2015.12.34676(223-228)Online publication date: 2015

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