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Carnegie Mellon Robotics Institute

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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.

Notes

Associated Center(s) / Consortia: Medical Robotics Technology Center

Associated Lab(s) / Group(s): Surgical Mechatronics Laboratory

Associated Project(s): Micron: Intelligent Microsurgical Instruments and ASAP

Text Reference

Brian Becker, Harsha Tummala, and Cameron Riviere, "Autoregressive Modeling of Physiological Tremor under Microsurgical Conditions," 30th Annual International IEEE EMBS Conference, August, 2008, pp. 1948-1951.

BibTeX Reference

@inproceedings{Becker_2008_6447,
   author = "Brian Becker and Harsha Tummala and Cameron Riviere",
   title = "Autoregressive Modeling of Physiological Tremor under Microsurgical Conditions",
   booktitle = "30th Annual International IEEE EMBS Conference",
   pages = "1948-1951",
   month = "August",
   year = "2008",
}

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