Una actividad física eficiente ante el reto de una sociedad de jóvenes influenciada por el modernismo y la tecnología

Actualmente los jóvenes se encuentran influenciados por un estilo de vida dominado por la modernidad y los avances tecnológicos, lo que deriva en una vida sedentaria que produce efectos nocivos en todos los sentidos y todos los ámbitos. Esto, aunado a los programas de un sistema institucional educativo y deportivo con innumerables carencias, nos lleva a una situación más complicada, en la que se añaden factores perjudiciales que abarcan la ignorancia, la incapacidad y los intereses personales, que no nos ayudan a edificar alternativas que comiencen a funcionar. Es primordial el establecimiento de una solución pronta y efectiva, encaminada a sistematizar una actividad física adecuada, basada en argumentos científicos y métodos acordes, que nos provean los diversos beneficios que su práctica otorga y nos ayuden a detener el proceso de deterioro físico, al cual el ritmo de vida actual no arroja

ChildCI Framework: Analysis of Motor and Cognitive Development in Children-Computer Interaction for Age Detection

This article presents a comprehensive analysis of the different tests proposed in the recent ChildCI framework, proving its potential for generating a better understanding of children's neuromotor and cognitive development along time, as well as their possible application in other research areas such as e-Health and e-Learning. In particular, we propose a set of over 100 global features related to motor and cognitive aspects of the children interaction with mobile devices, some of them collected and adapted from the literature. Furthermore, we analyse the robustness and discriminative power of the proposed feature set including experimental results for the task of children age group detection based on their motor and cognitive behaviors. Two different scenarios are considered in this study: i) single-test scenario, and ii) multiple-test scenario. Results over 93% accuracy are achieved using the publicly available ChildCIdb_v1 database (over 400 children from 18 months to 8 years old), proving the high correlation of children's age with the way they interact with mobile devices.Comment: 11 pages, 2 figures, 6 table

Dismorfia muscular y uso de sustancias ergogénicas. Una revisión sistemática.

El uso de sustancias ergogénicas (USE) no se restringe a la consecución de un mayor desempeño atlético, actualmente también es una conducta de cambio corporal, vía el desarrollo muscular; no obstante, poco se sabe de la relación entre dismorfia muscular (DM) y USE. Por tanto se realizó una revisión sistemática de los estudios empíricos que, durante la última década (2004-2014), la han examinado. De entrada, destaca el hecho de que, de los 22 artículos analizados, solo en 13 se explicita este interés. Además, aunque los datos documentados delinean algunas vertientes relevantes, como la existencia de una alta concomitancia (60-90%) de DM y USE, en general las evidencias son aún incipientes e inciertas, principalmente debido a la gran disparidad metodológica entre estudios y, particularmente, en cuanto a los indicadores, los parámetros y las medidas que, en el contexto de la DM, se han venido empleando para evaluar USE

Children Age Group Detection based on Human-Computer Interaction and Time Series Analysis

This article proposes a novel Children-Computer Interaction (CCI) approach for the task of age group detection. This approach focuses on the automatic analysis of the time series generated from the interaction of the children with mobile devices. In particular, we extract a set of 25 time series related to spatial, pressure, and kinematic information of the children interaction while colouring a tree through a pen stylus tablet, a specific test from the large-scale public ChildCIdb database. A complete analysis of the proposed approach is carried out using different time series selection techniques to choose the most discriminative ones for the age group detection task: i) a statistical analysis, and ii) an automatic algorithm called Sequential Forward Search (SFS). In addition, different classification algorithms such as Dynamic Time Warping Barycenter Averaging (DBA) and Hidden Markov Models (HMM) are studied. Accuracy results over 85% are achieved, outperforming previous approaches in the literature and in more challenging age group conditions. Finally, the approach presented in this study can benefit many children-related applications, for example, towards an age-appropriate environment with the technology.Comment: 10 pages, 6 figures, 6 tables, 32 reference

Compressed kNN: K-Nearest Neighbors with Data Compression

The kNN (k-nearest neighbors) classification algorithm is one of the most widely used non-parametric classification methods, however it is limited due to memory consumption related to the size of the dataset, which makes them impractical to apply to large volumes of data. Variations of this method have been proposed, such as condensed KNN which divides the training dataset into clusters to be classified, other variations reduce the input dataset in order to apply the algorithm. This paper presents a variation of the kNN algorithm, of the type structure less NN, to work with categorical data. Categorical data, due to their nature, can be compressed in order to decrease the memory requirements at the time of executing the classification. The method proposes a previous phase of compression of the data to then apply the algorithm on the compressed data. This allows us to maintain the whole dataset in memory which leads to a considerable reduction of the amount of memory required. Experiments and tests carried out on known datasets show the reduction in the volume of information stored in memory and maintain the accuracy of the classification. They also show a slight decrease in processing time because the information is decompressed in real time (on-the-fly) while the algorithm is running

A Review of Infrastructures to Process Big Multimedia Data

In the last years, the volume of information is growing faster than ever before, moving from small to huge, structured to unstructured datasets like text, image, audio and video. The purpose of processing the data is aimed to extract relevant information on trends, challenges and opportunities; all these studies with large volumes of data. The increase in the power of parallel computing enabled the use of Machine Learning (ML) techniques to take advantage of the processing capabilities offered by new architectures on large volumes of data. For this reason, it is necessary to find mechanisms that allow classify and organize them to facilitate to the users the extraction of the required information. The processing of these data requires the use of classification techniques that will be reviewed. This work analyzes different studies carried out on the use of ML for processing large volumes of data (Big Multimedia Data) and proposes a classification, using as criteria, the hardware infrastructures used in works of machine learning parallel approaches applied to large volumes of data

Child-Computer Interaction: Recent Works, New Dataset, and Age Detection

We overview recent research in Child-Computer Interaction and describe our framework ChildCI intended for: i) generating a better understanding of the cognitive and neuromotor development of children while interacting with mobile devices, and ii) enabling new applications in e-learning and e-health, among others. Our framework includes a new mobile application, specific data acquisition protocols, and a first release of the ChildCI dataset (ChildCIdb v1), which is planned to be extended yearly to enable longitudinal studies. In our framework children interact with a tablet device, using both a pen stylus and the finger, performing different tasks that require different levels of neuromotor and cognitive skills. ChildCIdb comprises more than 400 children from 18 months to 8 years old, considering therefore the first three development stages of the Piaget's theory. In addition, and as a demonstration of the potential of the ChildCI framework, we include experimental results for one of the many applications enabled by ChildCIdb: children age detection based on device interaction. Different machine learning approaches are evaluated, proposing a new set of 34 global features to automatically detect age groups, achieving accuracy results over 90% and interesting findings in terms of the type of features more useful for this task

Optimización del manual de mantenimiento enfocado en los problemas de calibración del equipo CNC Rottler EM105H del taller CRC de La Joya mediante el uso del método FMEA y de la herramienta láser Renishaw QC20.

El presente trabajo de investigación está enfocado en optimizar el manual de mantenimiento de calibración del equipo CNC Rottler EM105H del taller del Centro de Recuperación de Componentes La Joya. El enfoque integrado va dirigido a aumentar la confiabilidad del equipo CNC, minimizar tiempos de inactividad y optimizar los recursos del taller. Para esta finalidad, se ha identificado componentes críticos de la máquina y se ha realizado el análisis modal de fallos y efecto (FMEA). Con este estudio se ha determinado las fallas de mayor impacto en la calibración. De la misma forma se ha definido las tolerancias de cada componente mecánico crítico con información del fabricante. Estos parámetros definen los valores máximo y mínimo que se permiten variar en una pieza respecto a lo fabricado. Para evaluar los resultados se hizo uso de una herramienta de diagnóstico para CNC como es el Bállbar QC20 que muestra el error de circularidad respecto a un diámetro programado. Como resultado se evidencia una mejora de error de circularidad de 0.17 mm a 0.030 mmThe present investigation project is focused on optimizing the maintenance manual, specifically addressing calibration issues related to the CNC equipment Rottler EM105H in the workshop of the Component Recovery Center La Joya. The integrated approach aims to enhance the reliability of the CNC equipment, minimize downtime, and optimize workshop resources. For this purpose, critical components of the machine have been identified, and a Failure Modes and Effects Analysis (FMEA) has been conducted. This study has determined the most impactful failures in calibration. Likewise, tolerances for each critical mechanical component have been defined using manufacturer-provided information. These parameters establish the maximum and minimum values allowed to vary in a part relative to its manufactured specifications. To assess the results, a CNC diagnostic tool, the Bállbar QC20, was utilized. This tool indicates circularity error concerning a programmed diameter. The outcome reveals an improvement in circularity error from 0.17 mm to 0.030 mm.Trabajo de suficiencia profesiona

Next Generation of Energy Residential Gateways for Demand Response and Dynamic Pricing

Predictions about electric energy needs, based on current electric energy models, forecast that the global energy consumption on Earth for 2050 will double present rates. Using distributed procedures for control and integration, the expected needs can be halved. Therefore implementation of Smart Grids is necessary. Interaction between final consumers and utilities is a key factor of future Smart Grids. This interaction is aimed to reach efficient and responsible energy consumption. Energy Residential Gateways (ERG) are new in-building devices that will govern the communication between user and utility and will control electric loads. Utilities will offer new services empowering residential customers to lower their electric bill. Some of these services are Smart Metering, Demand Response and Dynamic Pricing. This paper presents a practical development of an ERG for residential buildings