Real-Time Detection of Task Switches of Desktop Users

Abstract

Desktop users commonly work on multiple tasks. The TaskTracer system provides a convenient, low-cost way for such users to define a hierarchy of tasks and to associate resources with those tasks. With this information, TaskTracer then supports the multi-tasking user by configuring the computer for the current task. To do this, it must detect when the user switches the task and identify the user's current task at all times. This problem of task switch detection is a special case of the general problem of change-point detection. It involves monitoring the behavior of the user and predicting in real time when the user moves from one task to another. We present a framework that analyzes a sequence of observations to detect task switches. First, a classifier is trained discriminatively to predict the current task based only on features extracted from the window in focus. Second, multiple single-window predictions (specifically, the class probability estimates) are combined to obtain more reliable predictions. This paper studies three such combination methods: (a) simple voting, (b) a likelihood ratio test that assesses the variability of the task probabilities over the sequence of windows, and (c) application of the Viterbi algorithm under an assumed task transition cost model. Experimental results show that all three methods improve over the single-window predictions and that the Viterbi approach gives the best results.