A Probabilistic Interpretation of Motion Correlation Selection Techniques


Motion correlation interfaces are those that present targets moving in different patterns, which the user can select by matching their motion. In this paper, we re-formulate the task of target selection as a probabilistic inference problem. We demonstrate that previous interaction techniques can be modelled using a Bayesian approach and that how modelling the selection task as transmission of information can help us make explicit the assumptions behind similarity measures. We propose ways of incorporating uncertainty into the decision-making process and demonstrate how the concept of entropy can illuminate the measurement of the quality of a design. We apply these techniques in a case study and suggest guidelines for future work.

Eduardo Velloso
University of Melbourne, Melbourne, Victoria, Australia
Carlos H. Morimoto
University of São Paulo, São Paulo, SP, Brazil





会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)

セッション: Computational Physical Interaction

[A] Paper Room 02, 2021-05-10 17:00:00~2021-05-10 19:00:00 / [B] Paper Room 02, 2021-05-11 01:00:00~2021-05-11 03:00:00 / [C] Paper Room 02, 2021-05-11 09:00:00~2021-05-11 11:00:00
Paper Room 02
12 件の発表
2021-05-10 17:00:00
2021-05-10 19:00:00