抄録
In the current office environment connected to the Internet, a user tends to get a lot of notifications in the form of e-mails, micro-blogs, instant messages, application update alerts, and so on. A significant problem with such notifications is that they arrive as they are sent, i.e., without the system being aware of whether the user has time to receive them or not. If messages arrive at inopportune times, they can cause serious stress and reduce the user's productivity. One way of alleviating this problem would be to control the notification period in accordance with the user's state of activity. In other words, this means a system needs to estimate whether a user is interruptible or not and to send information only when he/she is interruptible There are a number of studies to estimate use state (e.g. monitor user behaviors like typing, operating a mouse, facial expression and so on). However, these methods has some problems. In this paper, we develop a novel method for estimating user states. Our method use a pressure 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.
