C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Predicting human interruptibility with sensors
ACM Transactions on Computer-Human Interaction (TOCHI)
Effects of intelligent notification management on users and their tasks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ACM Transactions on Computer-Human Interaction (TOCHI)
Identifying emotional states using keystroke dynamics
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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A user working with his/her desktop computer would benefit from notifications (e.g., the arrival of e-mail, micro-blogs, and application updates) being given at adequate times when he/she is interruptible. To do so, a notification system needs to determine the user's state of activity. In this paper, we propose a novel method for estimating user states with a pressure sensor on a desk. We use a lattice-like pressure sensor sheet and distinguish between two simple user states: interruptible or not. The pressure can be measured without the user being aware of it, and changes in the pressure reflect useful information such as typing, an arm resting on the desk, mouse operation, and so on. We carefully developed features that can be extracted from the sensed raw data, and we used a machine learning technique to identify the user's interruptibility. We conducted experiments for two different tasks to evaluate the accuracy of our proposed method and obtained promising results.