Asier Aztiria

TL;DR: This work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behaviouralmodalities, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system.

TL;DR: The development of an unobtrusive and transparent AD detection system should be based on a multimodal system in order to take full advantage of all kinds of symptoms, detect even the smallest changes and combine them, so as to detect AD as early as possible.

TL;DR: This work identifies characteristics of Ambient Intelligence environments that have to be taken into account during the learning process and uses them to highlight the strengths and weaknesses of developments so far, providing direction to encourage further development in this specific area of Ambients Intelligence.

TL;DR: The goal of this paper is to evaluate the possibility of using unobtrusively collected activity-aware smart home behavior data to detect the multimodal symptoms that are often found to be impaired in AD.

TL;DR: This article presents a system that takes information collected by sensors as a starting point and then discovers frequent relationships between actions carried out by the user, supported by a language to represent those patterns and a system of interaction that provides the user the option to fine tune their preferences in a natural way, just by speaking to the system.