title: Autoregressive nodeling of physiological tremor under microsurgical conditions.
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.
authors.affiliation: Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA. briancbecker@cmu.edu
authors.forename: Brian C
authors.initials: BC
authors.lastname: Becker
authors.forename: Harsha
authors.initials: H
authors.lastname: Tummala
authors.forename: Cameron N
authors.initials: CN
authors.lastname: Riviere
mesh_terms: D001698:Biomedical Engineering; D004867:Equipment Design; D006225:Hand; D006801:Humans; D008866:Microsurgery / Q000009:adverse effects / Q000295:instrumentation* / Q000706:statistics & numerical data*; D008954:Models, Biological; D009068:Movement; D012044:Regression Analysis; D012371:Robotics / Q000295:instrumentation / Q000706:statistics & numerical data; D025321:Surgery, Computer-Assisted / Q000295:instrumentation / Q000706:statistics & numerical data; D014202:Tremor / Q000503:physiopathology*