Target prediction for icon clicking by athetoid persons

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.