2021/02/08 by Eduardo Velloso, Carlos Hitoshi Morimoto, Carlos H. Morimoto · 1 citation
Computer Science · Engineering · #Artificial intelligence #Bayesian inference #Bayesian probability #Computer science #Data Visualization and Analytics #Data mining #Engineering #Entropy (arrow of time) #Human Motion and Animation #Inference #Interactive and Immersive Displays #Machine learning #Probabilistic logic #Selection (genetic algorithm) #Task (project management) #cs.HC
paper · pdf · doi:10.1145/3411764.3445184
arxiv created 2021/02/08 · arxiv updated 2021/02/09 · openalex publication_date 2021/05/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.