The problems derived from the ageing of population in developed countries are a serious threat to the sustainability of the welfare state. As ageing related health expenses are continuously growing, it is mandatory to explore new ways towards active and independent ageing. One of the ways to achieve those objectives is to apply information and communication technologies (ICT) to develop automatic assistive systems (the Ambient Assisted Living or AAL paradigm). Such automatic assistive systems are designed to help people performing the so called Activities of Daily Living (ADL), as a person who can accomplish ADLs is said to be independent. In consequence, assistive systems should work on any environment where elderlies perform their daily activities, i.e. in urban environments. However, current systems are usually limited to more controlled environments as homes. The base of an assistive system is to properly understand the situation of an elder, in order to provide the needed assistance. We argue that current systems cannot work in urban or non-structured environments because their situational awareness approach is not suitable for such environments. Thus, this project proposes novel research on situational awareness systems for AAL. First of all, we propose to use wearable cameras for elderlies to avoid any dependency with the environment.

The University of Deusto has worked on the creation of new generalization mechanisms that will allow the adoption of behavior modelling and activity recognition models by enabling the cross-environment usage of trained models. We have also developed a novel change-point detection algorithm that improves the state of the art when dealing with continuous sensor streams. Finally, we have developed zero-shot algorithms focused on action recognition.