User behavior shift detection in ambient assisted living environments

Identifying users' frequent behaviors is considered a key step to achieving real, intelligent environments that support people in their daily lives. These patterns can be used in many different applications. An algorithm that compares current behaviors of users with previously discovered frequent behaviors has been developed. In addition, it identifies the differences between both behaviors. Identified shifts can be used not only to adapt frequent behaviors, but also shifts may indicate initial signs of some diseases linked to behavioral modifications, such as depression or Alzheimer’s. The algorithm was validated using datasets collected from smart apartments where five different ADLs (Activities of Daily Living) were recognized. It was able to identify all shifts from frequent behaviors, as well as identifying necessary modifications in all cases

Context-awareness and uncertainty: current scenario and challenges for the future

One of the main aspects of Ambient Intelligence (AmI) refers to its capacity of act autonomously in benefit of human beings. This implies in a hard challenge to overcome and of enormous responsibility. AmI is directly related to other fields of knowledge such as Smart environments, which aim to improve user experience through the development of context-aware applications. In this paper we present the current scenario of context-aware systems with some conceptual metrics to be followed. We highlight the problem of dealing with uncertain context information, e.g. incomplete, out-dated or nebulous data, seen as one of the main obstacle in this area.This work has been supported by COMPETE: POCI-01-0145-FE\-DER-007043 and FCT –Fundação para a Ciên-cia e Tecnologia within the Project Scope UID/CEC/ 00319/2013

Facilitating Preference Revision through a Spoken Dialogue System

We present the design of a spoken dialogue system to provide feedback to users of an autonomous system which can learn different patterns associated with user actions. Our speech interface allows users to verbally refine these patterns, giving the system his/her feedback about the accuracy of the actions learnt.We focus on improving the naturalness of user interventions, using a stochastic language model and a rule-based language understanding module. The development of a state-based di- alogue manager which decides how to conduct each dialogue, together with the storage of contextual information of previous dialogue turns, allows the user to speak to the system in a highly natural way

A survey on the evolution of the notion of context-awareness

The notion of Context has been considered for a long time in different areas of Computer Science. This article considers the use of context-based reasoning from the earlier perspective of AI as well as the newer developments in Ubiquitous Computing. Both communities have been somehow interested in the potential of context-reasoning to support real-time meaningful reactions from systems. We explain how the concept evolved in each of these different approaches. We found initially each of them considered this topic quite independently and separated from each other, however latest developments have started to show signs of cross-fertilization amongst these areas. The aim of our survey is to provide an understanding on the way context and context-reasoning were approached, to show that work in each area is complementary, and to highlight there are positive synergies arising amongst them. The overarching goal of this article is to encourage further and longer-term synergies between those interested in further understanding and using context-based reasoning