Edinburgh Research Explorer

Modeling 3D tremor signals with a quaternion weighted Fourier Linear Combiner

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

  • Kabita Adhikari
  • Sivanagaraja Tatinati
  • Kalyana C. Veluvolu
  • K. Nazarpour

Related Edinburgh Organisations

Original languageEnglish
Title of host publication2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages799-802
Number of pages4
ISBN (Electronic)978-1-4673-6389-1
DOIs
Publication statusPublished - 2 Jul 2015
Event7th International IEEE EMBS Neural Engineering Conference - Montpellier, France
Duration: 22 Apr 201524 Apr 2015
https://neuro.embs.org/2015/

Publication series

Name
PublisherIEEE
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference7th International IEEE EMBS Neural Engineering Conference
Abbreviated titleNER 2015
CountryFrance
CityMontpellier
Period22/04/1524/04/15
Internet address

Abstract

Physiological tremor is an involuntary and rhythmic movement of the body specially the hands. The vibrations in hand-held surgical instruments caused by physiological tremor can cause unacceptable imprecision in microsurgery. To rectify this problem, many adaptive filtering-based methods have been developed to model the tremor to remove it from the tip of microsurgery devices. The existing tremor modeling algorithms such as the weighted Fourier Linear Combiner (wFLC) algorithm and its extensions operate on the x, y, and z dimensions of the tremor signals independently. These algorithms are blind to the dynamic couplings between the three dimensions. We hypothesized that a system that takes these coupling information into account can model the tremor with more accuracy compared to the existing methods. Tremor data was recorded from five novice subjects and modeled with a novel quaternion weighted Fourier Linear Combiner (QwFLC). We compared the modeling performance of the proposed QwFLC with that of the conventional wFLC algorithm. Results showed that QwFLC improves the modeling performance by about 20% at the cost of higher computational complexity.

Event

7th International IEEE EMBS Neural Engineering Conference

22/04/1524/04/15

Montpellier, France

Event: Conference

ID: 164608280