Legislación y normas técnicas de accesibilidad en tecnologías de la información y las comunicaciones

Dentro de los instrumentos que posee la sociedad para lograrla inclusión de todas las personas destacan la legislación y las normas técnicas. En este capítulo se recopilan actividades legislativas y de normalizaciones, tanto internacionales como nacionales para promover una sociedad de la información inclusiva para todos. Dentro de las acciones legislativas destacan la sección 508 del Acta de Rehabilitación de Estados Unidos, los planes eEuropa de la Unión Europea y las leyes españolas de Servicios de la Sociedad de la Información y de Comercio Electrónico, y de Igualdad de Oportunidades, No Discriminación y Accesibilidad Universal de las Personas con. Discapacidad. En cuanto a las actividades de normalización técnica, destacan las normas internacionales de accesibilidad al software de ISO y de accesibilidad web del Consorcio de la Web, así como las normas españolas de accesibilidad en informática: hardware, software y contenidos web

Generating time series reference models based on event analysis

Creating a reference model that represents a given set of time series is a relevant problem as it can be applied to a wide range of tasks like diagnosis, decision support, fraud detection, etc. In some domains, like seismography or medicine, the relevant information contained in the time series is concentrated in short periods of time called events. In this paper, we propose a technique for generating time series reference models based on the analysis of the events they contain. The proposed technique has been applied to time series from two medical domains: Electroencephalography, a neurological procedure to record the electrical activity produced by the brain and Stabilometry, a branch of medicine studying balance-related functions in human beings

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals

Modelling Stabilometric Time Series

Stabilometry is a branch of medicine that studies balance-related human functions. Stabilometric systems generate time series. The analysis of these time series using data mining techniques can be very useful for domain experts. In the field of stabilometry, as in many other domains, the key nuggets of information in a time series are concentrated within definite time periods known as events. In this paper, we propose a technique for creating reference models for stabilometric time series based on event analysis. After testing the technique on time series recorded by top-competition sportspeople, we conclude that stabilometric models can be used to classify individuals by their balance-related abilitie

Event-based time series data preprocessing: application to traffic flow time series

Traffic flow time series data are usually high dimensional and very complex. Also they are sometimes imprecise and distorted due to data collection sensor malfunction. Additionally, events like congestion caused by traffic accidents add more uncertainty to real-time traffic conditions, making traffic flow forecasting a complicated task. This article presents a new data preprocessing method targeting multidimensional time series with a very high number of dimensions and shows its application to real traffic flow time series from the California Department of Transportation (PEMS web site). The proposed method consists of three main steps. First, based on a language for defining events in multidimensional time series, mTESL, we identify a number of types of events in time series that corresponding to either incorrect data or data with interference. Second, each event type is restored utilizing an original method that combines real observations, local forecasted values and historical data. Third, an exponential smoothing procedure is applied globally to eliminate noise interference and other random errors so as to provide good quality source data for future work

Balance and Postural Control Assess in Elite Ice Skaters

Análisis de curvas de fuerza muscular en patinadores de élite contando con el apoyo de sistemas de soporte a la decisión y técnicas de descubrimiento de conocimiento

Comparing Time Series Through Event Clusterin

The comparison of two time series and the extraction of subsequences that are common to the two is a complex data mining problem. Many existing techniques, like the Discrete Fourier Transform (DFT), offer solutions for comparing two whole time series. Often, however, the important thing is to analyse certain regions, known as events, rather than the whole times series. This applies to domains like the stock market, seismography or medicine. In this paper, we propose a method for comparing two time series by analysing the events present in the two. The proposed method is applied to time series generated by stabilometric and posture graphic systems within a branch of medicine studying balance-related functions in human beings

A Language for Defining Events in Multi-Dimensional Time Series: Application to a Medical Domain

In many domains, like seismography or medicine, time series analysis focuses on particular regions of interest in the time series, known as events, whereas the remainder of the times series contains hardly any useful information. Research into the field of time series events definition has proposed techniques that are only applicable to specific domains. In this paper, we propose an events definition language that is general enough to be able to simply and naturally define time series events in any domain. The proposed language has been applied to the definition of time series events generated by stabile metric systems within the branch of medicine dealing with balance related functions in human beings

Generating reference models for structurally complex data: application to the stabilometry medical domain

We present a framework specially designed to deal with structurally complex data, where all individuals have the same structure, as is the case in many medical domains. A structurally complex individual may be composed of any type of singlevalued or multivalued attributes, including time series, for example. These attributes are structured according to domain-dependent hierarchies. Our aim is to generate reference models of population groups. These models represent the population archetype and are very useful for supporting such important tasks as diagnosis, detecting fraud, analyzing patient evolution, identifying control groups, etc

Modelling Medical Time Series Using Grammar-Guided Genetic Programming

The analysis of time series is extremely important in the field of medicine, because this is the format of many medical data types. Most of the approaches that address this problem are based on numerical algorithms that calculate distances, clusters, reference models, etc. However, a symbolic rather than numerical analysis is sometimes needed to search for the characteristics of time series. Symbolic information helps users to efficiently analyse and compare time series in the same or in a similar way as a domain expert would. This paper describes the definition of the symbolic domain, the process of converting numerical into symbolic time series and a distance for comparing symbolic temporal sequences. Then, the paper focuses on a method to create the symbolic reference model for a certain population using grammar-guided genetic programming. The work is applied to the isokinetics domain within an application called I4