Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Estudio del Business Intelligence y desarrollo de un Cuadro de Mando para el Análisis de Ventas

El Business Intelligence ha pasado en los últimos 20 años de ser un capricho de unos pocos CIO, que podían permitirse destinar partidas presupuestarias para tal efecto, a convertirse en una realidad ya presente en muchas de las grandes empresas o una necesidad urgente para las que todavía no han implantado un sistema de esas características.La primera parte del presente documento, denominada “Estudio del Business Intelligence”, presenta una introducción a dicho concepto, desde la base. Explicando los conceptos teóricos clave necesarios para entender este tipo de soluciones, más adelante se comentan los componentes tecnológicos que van desde los procesos de extracción e integración de información a cómo debemos estructurar la información para facilitar el análisis. Por último, se repasan los diferentes tipos de aplicaciones que existen en el mercado así como las tendencias más actuales en este campo.La segunda parte del documento centra su foco en la implantación de un Cuadro de Mandos para el análisis de las ventas de una empresa, se identifican las diferentes fases del proyecto así como se entra en detalle de los requerimientos identificados. En último lugar, se presenta el desarrollo realizado del Cuadro de Mandos con tecnología Xcelsius, que permite exportar a flash el resultado y visualizarlo en cualquier navegador web.

Ambient intelligence application based on environmental measurements performed with an assistant mobile robot

This paper proposes the use of an autonomous assistant mobile robot in order to monitor the environmental conditions of a large indoor area and develop an ambient intelligence application. The mobile robot uses single high performance embedded sensors in order to collect and geo-reference environmental information such as ambient temperature, air velocity and orientation and gas concentration. The data collected with the assistant mobile robot is analyzed in order to detect unusual measurements or discrepancies and develop focused corrective ambient actions. This paper shows an example of the measurements performed in a research facility which have enabled the detection and location of an uncomfortable temperature profile inside an office of the research facility. The ambient intelligent application has been developed by performing some localized ambient measurements that have been analyzed in order to propose some ambient actuations to correct the uncomfortable temperature profile.

Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.

OERopoly : A Game to Generate Collective Intelligence around OER

Collective Intelligence (CI ) is a phenomenon that emerges at the crossroads of three worlds: Open Educational Resources (OER), Web 2.0 technologies and Online Learning Communities. Building CI for the OER movement means capturing the richness of information, experiences, knowledge and resources, that the movement is constantly generating, in a way that they can be shared and reused for the benefit of the movement itself. The organisation of CI starts from collecting the knowledge and experiences of OER's practitioners and scholars in new creative forms, and then situating this knowledge in a collective 'pot' from where it can be leveraged with new 'intelligent' meanings and toward new 'intelligent' goals. This workshop is an attempt to do so by engaging participants in a CI experience, in which they will contribute to, and at the same time take something from, the existing CI around OER, Web 2.0 technologies and Online Learning Communities.

Análisis de datos de accidentes de tráfico mediante soluciones big data y business intelligence

Este proyecto de final de carrera corresponde al área de inteligencia artificial y representa un caso de uso que pretende utilizar datos reales referentes a accidentes de tráfico (datos de accidentes, muertos, heridos, etc.) y analizarlas conjuntamente con datos que puedan tener una posible relación con los accidentes como el parque de vehículos, las temperaturas de la zona de los accidentes, etc. con la finalidad de poder obtener las posibles relaciones causa-efecto.

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.

Ambient intelligence application based on environmental measurements performed with an assistant mobile robot

This paper proposes the use of an autonomous assistant mobile robot in order to monitor the environmental conditions of a large indoor area and develop an ambient intelligence application. The mobile robot uses single high performance embedded sensors in order to collect and geo-reference environmental information such as ambient temperature, air velocity and orientation and gas concentration. The data collected with the assistant mobile robot is analyzed in order to detect unusual measurements or discrepancies and develop focused corrective ambient actions. This paper shows an example of the measurements performed in a research facility which have enabled the detection and location of an uncomfortable temperature profile inside an office of the research facility. The ambient intelligent application has been developed by performing some localized ambient measurements that have been analyzed in order to propose some ambient actuations to correct the uncomfortable temperature profile.

A Markov chain analysis of emotional exchange in voice-to-voice communication: Testing for the mimicry hypothesis of emotional contagion

Mimicry is a central plank of the emotional contagion theory; however, it was only tested with facial and postural emotional stimuli. This study explores the existence of mimicry in voice-to-voice communication by analyzing 8,747 sequences of emotional displays between customers and employees in a call-center context. We listened live to 967 telephone inter-actions, registered the sequences of emotional displays, and analyzed them with a Markov chain. We also explored other propositions of emotional contagion theory that were yet to be tested in vocal contexts. Results supported that mimicry is significantly present at all levels. Our findings fill an important gap in the emotional contagion theory; have practical implications regarding voice-to-voice interactions; and open doors for future vocal mimicry research.

An fMRI Study of Emotional engagement in decicion-making

It has been suggested that decisionmaking depends on sensitive feelings associatedwith cognitive processing rather than cognitiveprocessing alone. From human lesions, we knowthe medial anterior inferior-ventral prefrontalcortex processes the sensitivity associated withcognitive processing, it being essentiallyresponsible for decision making.In this fMRI (functional Magnetic ResonanceImage) study 15 subjects were analyzed usingmoral dilemmas as probes to investigate the neuralbasis for painful-emotional sensitivity associatedwith decision making. We found that a networkcomprising the posterior and anterior cingulateand the medial anterior prefrontal cortex wassignificantly and specifically activated by painfulmoral dilemmas, but not by non-painful dilemmas.These findings provide new evidence that thecingulate and medial anterior prefrontal areinvolved in processing painful emotionalsensibility, in particular, when decision makingtakes place. We speculate that decision makinghas a cognitive component processed by cognitivebrain areas and a sensitivity component processedby emotional brain areas. The structures activatedsuggest that decision making depends on painfulemotional feeling processing rather than cognitiveprocessing when painful feeling processinghappens

The factorial structure of the 41-item version of the Screen for Child Anxiety Related Emotional Disorders (SCARED) in a Spanish population of 8 to 12 years-old

On this instrumental study we intend to analyse the factorial structure of the Screen for Child Anxiety Related Emotional Disorders (SCARED) in a Spanish sample using exploratory and confirmatory factorial analysis. As a second objective we intend to develop a short form of it for rapid screening and, finally, to analyze the reliabilities of both questionnaires. The SCARED was administered to a community sample of 1,508 children aged between 8 and 12 years. The sample was randomly split using half for the exploratory analysis and the other half for the confirmatory study. Furthermore a reduced version of the SCARED was developed using the SchmidLeiman procedure. Exploratory Factor Analysis yielded a four factor structure comprised of Somatic/panic, Generalized anxiety, Separation anxiety and Social phobia factors This structure was confirmed using Confirmatory Factor Analysis. The four factors, the full scale and the short scale showed good reliabilities. The results obtained seem to indicate that the Spanish version of the SCARED has good internal consistency, and along with other recent results, has a structure of four related factors that replicates the dimensions proposed for anxiety disorders by the DSM-IV-TR