Target prediction for icon clicking by athetoid persons

Kevin C. Olds, Sara Sibenaller, Rory A. Cooper, Dan Ding, Cameron Riviere

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

Abstract

We present an investigation into modeling of athetoid motion and prediction of user intent, for use in assistive computer interfaces during icon-clicking tasks. Data were recorded from three athetoid patients during unassisted icon-clicking trials with an isometric joystick. In order to facilitate development and testing of filter designs without the difficulty of repeated testing with human subjects, a quantitative model of the recorded patient data was developed using pseudoinverse methods. Using this model within the visuomotor control loop for the icon-clicking task, a prediction filter was then developed to reduce the target acquisition time. The filter is based on a novel "autoregressive stretching window" model which selects five data points evenly distributed across the input and output histories to predict the intended target, together with a second-order system that smoothes the movement of the cursor. On average, the filter demonstrated a reduction of target acquisition time by a factor of 2.7 in experiments with the patient models.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Robotics and Automation, ICRA 2008
Pages2043-2048
Number of pages6
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Robotics and Automation, ICRA 2008 - Pasadena, CA, United States
Duration: May 19 2008May 23 2008

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2008 IEEE International Conference on Robotics and Automation, ICRA 2008
CountryUnited States
CityPasadena, CA
Period5/19/085/23/08

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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