Andoni Arruti

TL;DR: A natural human computer interaction paradigm is proposed for persons with cognitive impairments such as Alzheimer's Disease that uses a realistic virtual character, rendered on a common television set, to play the role of a virtual personal assistant that shows reminders, notifications and performs short dialogues with the user.

TL;DR: This work is based on the selection of the appropriate single classifiers by means of an evolutionary algorithm, and different base classifiers, which have been chosen from different classifier families, are used as candidates in order to obtain variability in the classifications given.

TL;DR: A biophysical real-time stress identification and classification system, analysing two noninvasive signals, the galvanic skin response and the heart rate variability, is developed, able to detect and classify different stress stages only based on two non invasive signals.

TL;DR: A new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition by means of an integration of an estimation of distribution algorithm (EDA) in the first layer to select the optimal subset from the standard base classifiers.

TL;DR: A study performed to analyze different Machine Learning techniques validity in automatic speech emotion recognition area using a bilingual affective database and techniques based on evolutive algorithms to select speech feature subsets that optimize automatic emotion recognition success rate.