Predicting human interruptibility with sensors, in

A person seeking someone else’s attention is normally able to quickly assess how interruptible they are. This assessment allows for behavior we perceive as natural, socially appropriate, or simply polite. On the other hand, today’s computer systems are almost entirely oblivious to the human world they operate in, and typically have no way to take into account the interruptibility of the user. This paper presents a Wizard of Oz study exploring whether, and how, robust sensor-based predictions of interruptibility might be constructed, which sensors might be most useful to such predictions, and how simple such sensors might be. The study simulates a range of possible sensors through human coding of audio and video recordings. Experience sampling is used to simultaneously collect randomly distributed self-reports of interruptibility. Based on these simulated sensors, we construct statistical models predicting human interruptibility and compare their predictions with the collected self-report data. The results of these models, although covering a demographically limited sample, are very promising, with the overall accuracy of several models reaching about 78%. Additionally, a model tuned to avoiding unwanted interruptions does so for 90 % of its predictions, while retaining 75 % overall accuracy

QnA: Augmenting an Instant Messaging Client to Balance User Responsiveness and Performance

The growing use of Instant Messaging for social and work-related communication has created a situation where incoming messages often become a distraction to users while they are performing important tasks. Staying on task at the expense of responsiveness to IM buddies may portray the users as impolite or even rude. Constantly attending to IM, on the other hand, may prevent users from performing tasks efficiently, leaving them frustrated. In this paper we present a tool that augments a commercial IM client by automatically increasing the salience of incoming messages that may deserve immediate attention, helping users decide whether or not to stay on task. Categories and Subject Descriptors H.5.3 [Information Interfaces and Presentation]: Group an