Automatic and continuous user task analysis via eye activity

Abstract

A day in the life of a user can be segmented into a series of tasks: a user begins a task, becomes loaded perceptually and cognitively to some extent by the objects and mental challenge that comprise that task, then at some point switches or is distracted to a new task, and so on. Understanding the contextual task characteristics and user behavior in interaction can benefit the development of intelligent systems to aid user task management. Applications that aid the user in one way or another have proliferated as computing devices become more and more of a constant companion. However, direct and continuous observations of individual tasks in a naturalistic context and subsequent task analysis, for example the diary method, have traditionally been a manual process. We propose a method for automatic task analysis system, which monitors the user's current task and analyzes it in terms of the task transition, and perceptual and cognitive load imposed by the task. An experiment was conducted in which participants were required to work continuously on groups of three sequential tasks of different types. Three classes of eye activity, namely pupillary response, blink and eye movement, were analyzed to detect the task transition and non-transition states, and to estimate three levels of perceptual load and three levels of cognitive load every second to infer task characteristics. This paper reports statistically significant classification accuracies in all cases and demonstrates the feasibility of this approach for task monitoring and analysis.