Football Video Annotation Based on Player Motion Recognition Using Enhanced Entropy

This paper presents a semi-parametric Algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus perform a fast automatic football video annotation by looking at the enhance entropy variance within a series of shot frames. The entropy is extracted on the Hue parameter from the HSV color system, not as a global feature but in spatial domain to identify regions within a shot that will characterize a certain activity within the shot period. The second part of the algorithm works towards the identification of dominant color regions that could represent players and playfield for further activity recognition. Experimental Results shows that the proposed football video segmentation algorithm performs with high accuracy.

eFisioTrack: a telerehabilitation environment based on motion recognition using accelerometry

The growing demand for physical rehabilitation processes can result in the rising of costs and waiting lists, becoming a threat to healthcare services’ sustainability. Telerehabilitation solutions can help in this issue by discharging patients from points of care while improving their adherence to treatment. Sensing devices are used to collect data so that the physiotherapists can monitor and evaluate the patients’ activity in the scheduled sessions. This paper presents a software platform that aims to meet the needs of the rehabilitation experts and the patients along a physical rehabilitation plan, allowing its use in outpatient scenarios. It is meant to be low-cost and easy-to-use, improving patients and experts experience. We show the satisfactory results already obtained from its use, in terms of the accuracy evaluating the exercises, and the degree of users’ acceptance. We conclude that this platform is suitable and technically feasible to carry out rehabilitation plans outside the point of care.

Multi-agent system for control of robots inspired on the distributed activity and hormonal regulation of humans

Robotics is an emerging field with great activity. Robotics is a field that presents several problems because it depends on a large number of disciplines, technologies, devices and tasks. Its expansion from perfectly controlled industrial environments toward open and dynamic environment presents a many new challenges. New uses are, for example, household robots or professional robots. To facilitate the low cost, rapid development of robotic systems, reusability of code, its medium and long term maintainability and robustness are required novel approaches to provide generic models and software systems who develop paradigms capable of solving these problems. For this purpose, in this paper we propose a model based on multi-agent systems inspired by the human nervous system able to transfer the control characteristics of the biological system and able to take advantage of the best properties of distributed software systems. Specifically, we model the decentralized activity and hormonal variation.

Calcineurin expression and activity is regulated by the intracellular redox status and under hypertension in human neutrophils

Calcineurin (protein phosphatase 2B) (CN) comprises a family of serine/threonine phosphatases that play a pivotal role in signal transduction cascades in a variety of cells, including neutrophils. Angiotensin II (Ang II) increases both activity and de novo synthesis of CN in human neutrophils. This study focuses on the role that intracellular redox status plays in the induction of CN activity by Ang II. Both de novo synthesis of CN and activity increase promoted by Ang II were downregulated when cells were treated with l-buthionine-(S,R)-sulfoximine, an inhibitor of synthesis of the antioxidant glutathione. We have also investigated the effect of pyrrolidine dithiocarbamate and phenazine methosulfate, which are antioxidant and oxidant compounds, respectively, and concluded that the intracellular redox status of neutrophils is highly critical for Ang II-induced increase of CN expression and activity. Results obtained in neutrophils from hypertensive patients were very similar to those obtained in these cells on treatment with Ang II. We have also addressed the possible functional implication of CN activation in the development of hypertension. Present findings indicate that downregulation of hemoxygenase-1 expression in neutrophils from hypertensive subjects is likely mediated by CN, which acts by hindering translocation to the nucleus of the transcription factor NRF2. These data support and extend our previous results and those from other authors on modulation of CN expression and activity levels by the intracellular redox status.

OpeNER: open polarity enhanced named entity recognition

Actualmente existe una gran cantidad de empresas ofreciendo servicios para el análisis de contenido y minería de datos de las redes sociales con el objetivo de realizar análisis de opiniones y gestión de la reputación. Un alto porcentaje de pequeñas y medianas empresas (pymes) ofrecen soluciones específicas a un sector o dominio industrial. Sin embargo, la adquisición de la necesaria tecnología básica para ofrecer tales servicios es demasiado compleja y constituye un sobrecoste demasiado alto para sus limitados recursos. El objetivo del proyecto europeo OpeNER es la reutilización y desarrollo de componentes y recursos para el procesamiento lingüístico que proporcione la tecnología necesaria para su uso industrial y/o académico.

Evaluación del contenido sobre educación alimentaria en páginas web de servicios de catering: estudio piloto en el ámbito escolar

Introducción: Analizar la calidad de las páginas web de los servicios de catering en el ámbito escolar y su contenido en educación alimentaria, y tener una primera experiencia con la herramienta de evaluación EDALCAT. Material y métodos: Estudio descriptivo transversal. La población de estudio son páginas web de empresas de catering encargadas de la gestión de los comedores escolares. La muestra se obtuvo utilizando el buscador Google y un Ranking de las principales empresas de catering por facturación, escogiendo aquellas que tenían página web. Para la prueba piloto se seleccionaron diez páginas web según proximidad geográfica a la ciudad de Alicante y nivel de facturación. Para la evaluación de los sitios web se diseñó un cuestionario (EDALCAT), compuesto de un primer bloque de predictores de calidad con 19 variables de fiabilidad, diseño y navegación; y de un segundo bloque de contenidos específicos de educación alimentaria con 19 variables de contenido y actividades educativas. Resultados: Se han obtenido resultados positivos en 31 de las 38 variables del cuestionario, excepto en los ítems: “Buscador”, “Idioma” (40%) y “Ayuda” (10%) del bloque predictores de calidad y en los ítems: “Talleres”, “Recetario”, “Web alimentación-nutrición” (40%) y “Ejemplos” (30%) del bloque de contenidos específicos de educación alimentaria. Todas las páginas web evaluadas superan valores del 50% de cumplimiento de criterios de calidad y de contenidos mínimos en educación alimentaria, y sólo una de ellas, incumple el nivel de actividad mínimo establecido. Conclusiones: Los predictores de calidad y los contenidos específicos en educación alimentaria dieron buenos resultados en todas las páginas web evaluadas. La mayoría de ellas obtuvieron una alta puntuación en su valoración, y en su análisis individual por bloques. Tras el estudio piloto el cuestionario se ha modificado y se obtiene el EDALCAT definitivo. En líneas generales EDALCAT parece ser adecuado para evaluar la calidad de las páginas web de servicios de catering y su contenido en educación alimentaria, sin embargo el presente estudio no puede considerarse como validación del mismo.

GPLSI Compendium App

GPLSI Compendium App se trata de una aplicación móvil que hará las veces de un sistema de gestión y difusión de contenidos digitales, en la que el usuario podrá realizar resúmenes de textos de diferentes webs, tales como artículos o noticias, y posteriormente compartir esos resúmenes en sus redes sociales o a través del correo. Se le proporcionarán varios métodos de resumen, así como las descripciones y un pequeño tutorial de cómo utilizar la aplicación.

Human Behaviour Recognition based on Trajectory Analysis using Neural Networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.