Statistical estimation of user's interactions from motion impaired cursor use data

We report the application of new statistical state space filtering techniques to cursor movement data collected from motion impaired computer users performing a standard Fitts’s Law style selection task. Developed as an alternative to expensive haptic feedback assistance, the aim was to assess the feasibility of the basic techniques in resolving the users intended trajectory from the extremely variable and wavering data that result from the effects of muscular spasm, weakness and tremor. The results, using a choice of basic parameter for the filters, show that the state space filtering techniques are well suited to estimating the intended trajectory of the cursor even under conditions of extreme deviation from the direct track and that these filters effectively act as an extreme cursor smoothing system. We conclude that further development of the approach may lead to more effective adaptive systems capable of providing smoothed feedback to the user and estimates of intended destination. A similar approach might further be applied to situationally induced movement perturbations.

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