Pressure ulcers are skin injuries caused by long term exposition to high pressures on support points that interrupt blood circulation reducing the transport of oxygen and nutrients to the cells. They mainly affect people with poor mobility that stay in seating position for long periods of time. In spite of the diversity of commercial prototypes of cushions, ulcers caused by pressure are still a problem for wheelchair users. This work describes the design of a measurement system of pressure distribution in sedentary position. The aim of the system is to record the pressure concentration in order to obtain specific information about the supporting areas, and with these data used as feedback, eventually to determine an efficient random stimulation sequence to provide, in the future, a system to prevent these referred injuries. The proposed system consists of a 12 air-cell division cushion. Each cell has a pressure sensor and an input for electro valves to inflate and deflate. The recording and control of the valves is carried out through a graphical interface designed in LabVIEW®. A calibration procedure for the designed cushion was made by comparing the greatest load values pressure with a commercial platform, similar results were obtained.

Este trabajo aplica la metodología de enseñanza que tiene como sustento teórico el aprendizaje basado en proyectos (ABP) a una asignatura de ingeniería denominada Base de Datos, ya que se ha demostrado que las competencias colaborativas implicadas auxilian en la formación del estudiante. El uso de esta metodología, con base en la estrategia de aprendizaje para abordar problemáticas de la vida real de interés para el alumno, contribuye en el desarrollo profesional del ingeniero en formación. Una característica especial de la variante de ABP propuesta en esta investigación consiste en que la evaluación es continua y directamente aplicable a cada alumno que resuelve un problema con una aplicación práctica diferente. Otra diferencia es que, al desarrollar el proyecto a lo largo del semestre integrando los conocimientos adquiridos en forma gradual, a través de las 3 etapas, con retroalimentación constante, objetiva y sistemática, motiva al alumno a continuar con el proyecto. La planeación didáctica por parte del profesor considera actividades, resultados y rúbricas bien definidos, para después entrar en la primera etapa, que corresponde a la planeación del proyecto por parte del equipo e incluye tanto la determinación de los requerimientos, así como el diseño de la base de datos; la segunda etapa permite implementar la base de datos en un lenguaje adecuado, y, finalmente, la tercera etapa considera la integración de la base de datos y su manejo dentro del sistema de gestión. Los resultados del caso de estudio que se presentan corresponden al proceso de implementación ABP, a través de cada una de sus etapas, con un grupo de 30 estudiantes, en donde más del 80 % logró concluir su proyecto con un buen diseño, desarrollo e implementación, al cumplir con los requerimientos del sistema planteados. Esta estrategia les permitió a los estudiantes desarrollar habilidades de investigación, incrementando las capacidades de análisis y de síntesis y logrando una experiencia educativa motivada, con un alto índice de compromiso por parte de los involucrados para llegar al final.

This paper describes the methodology and results obtained when classifying two states of stress, low and high using a data base from Physionet that contains the recordings of physiological signals under several stress conditions. The signals were first denoised and then, features were extracted for segments of 5 minutes. Four out of 6 signals were chosen: heart rate variability, respiration, galvanic skin response from the hand, and galvanic skin response from the foot. Two non-linear features were extracted: approximate entropy and correlation dimension, both with m=2 and m=3. Besides, three linear features were extracted: energy, mean and standard deviation. Five machine learning classifiers were compared: K-nearest neighbours, Support vector machines with a linear kernel, support vector machines with a Gaussian kernel, Naïve Bayes classifier, Random forest classifier and logistic regression. It was found that approximate entropy and correlation dimension with m=3 provide the greater differences between the two stress states. It was also found that choosing only three physiological signals and correlation dimension with m=3 the logistic regression classifier achieved and accuracy of 81.38%, the best performance compared to other combinations of signals and classifiers. The three physiological signals that provided the best features were heart rate variability, respiration and galvanic skin response on the foot.

This paper presents the preliminary results of analysis of an electrogastrogram (EGG) database, as well as the procedure of its generation. The visual analysis of the EGG, denoised processes, filtering, wavelet analysis, reconstruction signal and classical parameters obtained are reported. Analysis of the data using these parameters corresponds to previously reported results as well as when they were compared with clinical evaluation. This database could be used in generation of intelligent systems for monitoring gastric performance as an aid for diagnosis.

The impact of the air pollution phenomenon has been long studied, but most often with a fragmented approach, without closely looking at the relationship between different components that characterize it, such as sensor-based data, health data from institutional databases, and data on how it is perceived by human beings in social media. The research developed in this study introduces an integrated methodological framework that analyses sensor data on air pollution distributed in space and time, combined with health data and social data narratives that reflect how different communities perceive this phenomenon in space and time; exploring how these different heterogeneous sources can be combined to better understand the impact of air pollution phenomena at the large-city level in the Valley of Mexico. We introduce a Spatio-temporal data integration and mining framework that aims to discover trends and insights regarding the distribution of the impact of an air pollution phenomenon in terms of human health and perception. The main peculiarity of our methodological framework is the integration of different large data sources by combining a series of methods: NLP (topic modeling), data mining (data cubes, unsupervised learning, and clustering), and GIS capabilities (spatial interpolation, choropleth maps) that together provide a better understanding of the quantitative and qualitative patterns emerging at a different spatial scale and temporal granularity. Overall, this shows how social data, when combined with quantitative data, can provide a better understanding of the impact of a given phenomenon, such as air pollution.