Automated Discovery of Human Activities inside Pervasive Living Spaces

The recognition and detection of human activities constitutes a very important step towards the fulfilment of the notion of pervasive environments. By detecting patterns on those behaviours, an environment can adapt and respond to the inhabitants' needs, thus improving the quality of life. This paper presents a framework in which those ideas can be applied and tested. It includes a system using a temporal neural-network driven embedded agent working with online, real-time data from a network of unobtrusive low-level sensors situated in either a simulated environment or a fully fitted real environment such as a whole flat