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Modeling intent and destination prediction within a Bayesian framework: Predictive touch as a usecase

Published version
Peer-reviewed

Type

Article

Change log

Authors

Gan, Runze 
Ahmad, Bashar I 
Godsill, Simon 

Abstract

jats:titleAbstract</jats:title> jats:pIn various scenarios, the motion of a tracked object, for example, a pointing apparatus, pedestrian, animal, vehicle, and others, is driven by achieving a premeditated goal such as reaching a destination. This is albeit the various possible trajectories to this endpoint. This paper presents a generic Bayesian framework that utilizes stochastic models that can capture the influence of intent (viz., destination) on the object behavior. It leads to simple algorithms to infer, as early as possible, the intended endpoint from noisy sensory observations, with relatively low computational and training data requirements. This framework is introduced in the context of the novel predictive touch technology for intelligent user interfaces and touchless interactions. It can determine, early in the interaction task or pointing gesture, the interface item the user intends to select on the display (e.g., touchscreen) and accordingly simplify as well as expedite the selection task. This is shown to significantly improve the usability of displays in vehicles, especially under the influence of perturbations due to road and driving conditions, and enable intuitive contact-free interactions. Data collected in instrumented vehicles are shown to demonstrate the effectiveness of the proposed intent prediction approach.</jats:p>

Description

Keywords

Destination prediction, human-computer interaction, intent modeling, Kalman filter, object tracking, particle filter, sequential Monte Carlo, touchless interaction

Journal Title

Data-Centric Engineering

Conference Name

Journal ISSN

2632-6736
2632-6736

Volume Title

1

Publisher

Cambridge University Press (CUP)