Attention Sensing through Multimodal User Modeling in an Augmented Reality Guessing Game

Abstract

We developed an attention-sensitive system that is capable of playing the children's guessing game "I spy with my litte eye" with a human user. In this game, the user selects an object from a given scene and provides the system with a single-sentence clue about it. For each trial, the system tries to guess the target object. Our approach combines top-down and bottom-up machine learning for object and color detection, automatic speech recognition, natural language processing, a semantic database, eye tracking, and augmented reality. Our evaluation demonstrates performance significantly above chance level, and results for most of the individual machine learning components are encouraging. Participants reported very high levels of satisfaction and curiosity about the system. The collected data shows that our guessing game generates a complex and rich data set. We discuss the capabilities and challenges of our system and its components with respect to multimodal attention sensing.