Adrian Basarab

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: In the case of non-Gaussian priors, it is shown how the analytical solution derived from the Gaussian case can be embedded into traditional splitting frameworks, allowing the computation cost of existing algorithms to be decreased significantly.

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 paper evaluates the method's feasibility on two emblematic cases: cardiac tagged magnetic resonance and cardiac ultrasound, and finds that the proposed framework provides, along with higher accuracy, superior robustness to noise and a considerably shorter computation time.