Multi-step prediction and filtering of pathological tremor for FES applications

  • Authors:

  • Sivanagaraja Tatinati;Kalyana C. Veluvolu;Wei Tech Ang

  • Affiliations:

  • Kyungpook National University, Daegu, South Korea;Kyungpook National University, Daegu, South Korea;Nanyang Technological University, Singapore

  • Venue:
  • Proceedings of the 7th International Convention on Rehabilitation Engineering and Assistive Technology
  • Year:
  • 2013

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Abstract

In this paper, a machine learning technique (LS-SVM) was employed for filtering and multistep prediction of pathological tremor to overcome the delay associated with the pre-filtering and electromechanical delay in FES applications. To validate the proposed approach, a study was conducted on the pathological tremor data collected form eight patients. Results show that LS-SVM provides better prediction accuracy compared to the earlier methods.