CUED Publications database

Probabilistic intentionality prediction for target selection based on partial cursor tracks

Ahmad, BI and Langdon, PM and Bunch, P and Godsill, SJ (2014) Probabilistic intentionality prediction for target selection based on partial cursor tracks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8515 L. pp. 427-438. ISSN 0302-9743

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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. © 2014 Springer International Publishing.

Item Type: Article
Divisions: Div C > Engineering Design
Div F > Signal Processing and Communications
Depositing User: Unnamed user with email
Date Deposited: 17 Jul 2017 19:16
Last Modified: 22 Oct 2019 05:41
DOI: 10.1007/978-3-319-07446-7_42