User state identification through desktop interaction

A user working at his/her desktop computer would benefit from notifications being given at timings that reflect their relevancy to the user´s activity and workload. To do so correctly, a notification system should have a way of determining the user´s state of activity We propose a novel method to estimate user states with a pressure sensor on a desk. We use a lattice-like pressure sensor sheet and distinguish between two simple user states: busy or idle. The pressure can be measured without the user being aware of it, and changes in the pressure reflect useful information like typing, an arm, the presence of a coffee mug, and so on. We carefully developed features which can be extracted from the sensed data and used a machine learning technique to identify the user state. We conducted experiments evaluating the accuracy of our method and obtained promising results.