TALISMAN+: Intelligent System for Follow-Up and Promotion of Personal Autonomy

The TALISMAN+ project, financed by the Spanish Ministry of Science and Innovation, aims to research and demonstrate innovative solutions transferable to society which offer services and products based on information and communication technologies in order to promote personal autonomy in prevention and monitoring scenarios. It will solve critical interoperability problems among systems and emerging technologies in a context where heterogeneity brings about accessibility barriers not yet overcome and demanded by the scientific, technological or social-health settings

CMS physics technical design report : Addendum on high density QCD with heavy ions

Peer reviewe

The ethics of using cameras in care homes

Co-author at Kingston University with relevant expertise around camera technologies.Exploration of the issues and pointer to ethical framework that is applicable to the use of assistive technologies (e.g. cameras) in care homes as a means of safeguarding residents and staff

Adaptive human action recognition with an evolving bag of key poses

Vision-based human action recognition allows to detect and understand meaningful human motion. This makes it possible to perform advanced human-computer interaction, among other applications. In dynamic environments, adaptive methods are required to support changing scenario characteristics. Specifically, in human-robot interaction, smooth interaction between humans and robots can only be performed if these are able to evolve and adapt to the changing nature of the scenarios. In this paper, an adaptive vision-based human action recognition method is proposed. By means of an evolutionary optimisation method, adaptive and incremental learning of human actions is supported. Through an evolving bag of key poses, which models the learnt actions over time, the current learning memory is developed to recognise increasingly more actions or actors. The evolutionary method selects the optimal subset of training instances, features and parameter values for each learning phase, and handles the evolution of the model. The experimentation shows that our proposal achieves to adapt to new actions or actors successfully, by rearranging the learnt model. Stable and accurate results have been obtained on four publicly available RGB and RGB-D datasets, unveiling the method’s robustness and applicability.This work has been partially supported by the European Commission under project “caring4U - A study on people activity in private spaces: towards a multisensor network that meets privacy requirements” (PIEF-GA-2010-274649) and by the Spanish Ministry of Science and Innovation under project “Sistema de visión para la monitorización de la actividad de la vida diaria en el hogar” (TIN2010-20510-C04-02). Alexandros Andre Chaaraoui acknowledges financial support by the Conselleria d’Educació, Formació i Ocupació of the Generalitat Valenciana (fellowship ACIF/2011/160)

Hand Gesture Recognition Based on Morphologic Features

Comunicación presentada en el IX Simposium Nacional de Reconocimiento de Formas y Análisis de Imágenes, Benicàssim, Mayo, 2001.In this paper, we try to characterize the hand posture by means of the morpho-logic coefficient, measure that allows to determine its morphology, in order to later perform its classification by means of an artificial neural network model, the Growing Cell Structure. The monitoring of the neurons that are activated for the successive postures that the hand takes throughout the time allows us to determine the gesture that is being made. Finally, we present the results of experiments with a vocabulary of 7 gestures, in order to verify the efficiency of this method of classification and extraction of characteristics