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- W2436819 abstract "Pointing tasks, for example to select an object in an interface, constitute a significant part of human-computer interactions. This motivated several studies into techniques that facilitate the pointing task and improve its accuracy. In this paper, we introduce a number of intentionality prediction algorithms to determine the intended target a priori from partial cursor tracks. They yield notable reductions in the pointing time, aid effective selection assistance routines and enhance the overall pointing accuracy. A number of benchmark prediction models are also restated within a statistical framework and their probabilistic interpretation is utilised to calculate their corresponding outcomes. The relative performance of all considered predictors is assessed for point-click task data sets pertaining to both able-bodied and impaired users. Bayesian adaptive filtering is deployed to smooth highly perturbed mouse cursor tracks that are typically produced by motor impaired users undertaking a pointing task." @default.
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- W2436819 date "2014-01-01" @default.
- W2436819 modified "2023-09-26" @default.
- W2436819 title "Probabilistic Intentionality Prediction for Target Selection Based on Partial Cursor Tracks" @default.
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- W2436819 doi "https://doi.org/10.1007/978-3-319-07446-7_42" @default.
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