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  • ISSN: 2278-0149 (Online)
  • Abbreviated Title:  Int. J. Mech. Eng. Robot. Res.  
  • Editor-in-Chief: ​Prof Richard (Chunhui) Yang, Western Sydney University, Australia
  • Associate Editor: Prof. B.V. Appa Rao, Andhra University; Prof. Ian R. McAndrew, Capitol Technology University, USA
  • Managing Editor: Murali Krishna. B
  • DOI: 10.18178/ijmerr
  • Abstracting/Indexing: Scopus (since 2016), CNKI, Google Scholar, Crossref, etc.
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International Journal of Mechanical Engineering and Robotics Research
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Muscle Tone Level Classification Based on Upper-Limb Impedance Model

Abstract—Many tools have been developed for the assessment of muscle tone of impaired limbs. Despite, having the appropriate knowledge, therapists still face with challenge in the assessment due to the subjective evaluation of muscle tone during training sessions. Moreover, the training has become more-costly and time consuming since the subjects have to face the therapists over a long period of time. By deploying robot-assisted system, some of these problems could have been addressed but the aspect of proper assessment of subjects’ muscle tone levels still remain. Assessment of subjects’ muscle tones allows proper prescription of task during training session. Recent studies have established links between muscle tone and upper-limb mechanical impedance. However the development of adequate estimation algorithm for subjects’ upper-limb impedance parameters and the prediction of muscle tone level in a more objective manner is still a subject of many research works. This study proposes an appropriate strategy for the estimation of upper-limb mechanical impedance parameters as a mean for the assessment of subjects’ muscle tone levels. Both simulation and experimental results show that the upper-limb impedance parameters can be estimated to a good accuracy level, while the subjects’ muscle tone level can be consistently predicted.

Index Terms—robot-assisted system; muscle tone; upper-limb impedance parameter; recursive least square estimator; ANN