Emulation of muscle tone of upper limb spasticity and rigidity

C. Z.Noor Ayuni, Takashi Komeda, Cheng Yee Low, Kaoru Inoue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Stretching the elbow of patients with upper limb disorder elicits diverse muscle tone responses, for instance, in an increased, uniform or jerky manner. This work addresses the emulation of muscle tone of the symptoms of spasticity, lead-pipe rigidity and cogwheel rigidity in the upper limb. A hybrid actuation strategy capable of emulating dynamic muscle tone has been proposed. In such strategy, a Magneto-Rheological brake is used to produce the resistance for a constant stiffness, and supported by a DC servo motor to assist the time delay of the fluid particle settling rate of the magneto-rheological fluid. The outcome is a consistent replication of the motion characteristics where the resistance torque emulated at the elbow joint is in close resemblance with the simulated data of upper limb spasticity and rigidity.

Original languageEnglish
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages1590-1593
Number of pages4
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: 2013 Nov 62013 Nov 8

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
CountryUnited States
CitySan Diego, CA
Period13/11/613/11/8

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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  • Cite this

    Ayuni, C. Z. N., Komeda, T., Low, C. Y., & Inoue, K. (2013). Emulation of muscle tone of upper limb spasticity and rigidity. In 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 (pp. 1590-1593). [6696252] (International IEEE/EMBS Conference on Neural Engineering, NER). https://doi.org/10.1109/NER.2013.6696252