On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

FTT-MA: A Flexible Time-Triggered Middleware Architecture for Time Sensitive, Resource-Aware AmI Systems

There is an increasing number of Ambient Intelligence (AmI) systems that are time-sensitive and resource-aware. From healthcare to building and even home/office automation, it is now common to find systems combining interactive and sensing multimedia traffic with relatively simple sensors and actuators (door locks, presence detectors, RFIDs, HVAC, information panels, etc.). Many of these are today known as Cyber-Physical Systems (CPS). Quite frequently, these systems must be capable of (1) prioritizing different traffic flows (process data, alarms, non-critical data, etc.), (2) synchronizing actions in several distributed devices and, to certain degree, (3) easing resource management (e.g., detecting faulty nodes, managing battery levels, handling overloads, etc.). This work presents FTT-MA, a high-level middleware architecture aimed at easing the design, deployment and operation of such AmI systems. FTT-MA ensures that both functional and non-functional aspects of the applications are met even during reconfiguration stages. The paper also proposes a methodology, together with a design tool, to create this kind of systems. Finally, a sample case study is presented that illustrates the use of the middleware and the methodology proposed in the paper.

Solutions for New Terrestrial Broadcasting Systems Offering Simultaneously Stationary and Mobile Services

221 p.

Nuevos métodos de cálculo de la profundidad de las compresiones torácicas a partir de la señal de aceleración.

[EN]These feedback devices are used to improve the quality of chest compressions while performing CPR technique, as they provide real time information to guide the rescuer during resuscitation attempts. Most feedback systems on the market are based on accelerometers and additional sensors or reference signals, used for calculating the displacement of the chest from the acceleration signal. This makes them expensive and complex devices. With the aim of optimizing these feedback systems and overcoming their limitations, in this document we propose three alternative methods for calculating the depth of chest compressions. These methods differ from the ones existing so far in that they use exclusively the chest acceleration signal to compute the displacement. With their implementation, it would be possible to develop systems to provide accurate feedback more easily and economically. In this context, this document details the design and implementation of the three methods and the development of a software environment to analyze the accuracy of each of them and compare the results by means of a detailed calculation of errors. Furthermore, in order to evaluate the methods a database is required, and it can be compiled using a sensorized manikin to record the acceleration signal and the gold standard chest compression depth. The database generated will be used for other studies related to the estimation of the compression depth, because the signals obtained in the manikin platform are very similar to those recorded during a real resuscitation episode.