Abstract

Mobile phones are increasingly equipped with sensors, such as accelerometers, GPS , proximity sensors and cameras, that can be used to sense and interpret people behaviour in real-time. Novel user-centered sensing applications can be built by exploiting the availability of such technologies in these devices that are part of our everyday experience. Data extracted from the sensors can also be used to model people behaviour and movement patterns providing a very rich set of multi-dimensional data, which can be extremely useful for social science and epidemiological studies.

In this talk I will present the design and implementation of the CenceMe platform, a system that supports the inference of activities and other presence information of individuals using off-the-shelf sensor-enabled mobile phones and sharing of this information through social networking information such as Facebook. I will also present an experimental prototype for sensing and mapping user activities by means of virtual avatars in Second Life. Finally, I will discuss how we are using these data to model people activity and movement patterns in the geographical space.