Autoregressive Modeling of Physiological Tremor under Microsurgical Conditions - Robotics Institute Carnegie Mellon University

Autoregressive Modeling of Physiological Tremor under Microsurgical Conditions

Brian Becker, Harsha Tummala, and Cameron Riviere
Conference Paper, Proceedings of 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '08), pp. 1948 - 1951, August, 2008

Abstract

Tremor was recorded under simulated vitreoretinal microsurgical conditions as subjects attempted to hold an instrument motionless. Several autoregressive models (AR, ARMA, multivariate, and nonlinear) are generated to predict the next value of tremor. It is shown that a sixth order ARMA model predictor can predict a tremor having an amplitude of 96.6 ± 84.5 microns RMS with an error of 8.2 ± 5.9 microns RMS, a mean improvement of 47.5% over simple last-value prediction.

BibTeX

@conference{Becker-2008-10071,
author = {Brian Becker and Harsha Tummala and Cameron Riviere},
title = {Autoregressive Modeling of Physiological Tremor under Microsurgical Conditions},
booktitle = {Proceedings of 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '08)},
year = {2008},
month = {August},
pages = {1948 - 1951},
}