Javier Medina

Fuzzy Intelligent System for Patients with Preeclampsia in Wearable Devices

Preeclampsia affects from 5% to 14% of all pregnant women and is responsible for about 14% of maternal deaths per year in the world. This paper is focused on the use of a decision analysis tool for the early detection of preeclampsia in women at risk. This tool applies a fuzzy linguistic approach implemented in a wearable device. In order to develop this tool, a real dataset containing data of pregnant women with high risk of preeclampsia from a health center has been analyzed, and a fuzzy linguistic methodology with two main phases is used. Firstly, linguistic transformation is applied to the dataset to increase the interpretability and flexibility in the analysis of preeclampsia. Secondly, knowledge extraction is done by means of inferring rules using decision trees to classify the dataset. The obtained linguistic rules provide understandable monitoring of preeclampsia based on wearable applications and devices. Furthermore, this paper not only introduces the proposed methodology, but also presents a wearable application prototype which applies the rules inferred from the fuzzy decision tree to detect preeclampsia in women at risk. The proposed methodology and the developed wearable application can be easily adapted to other contexts such as diabetes or hypertension.

Generation of a Partitioned Dataset with Single, Interleave and Multioccupancy Daily Living Activities

The advances in electronic devices have entailed the development of smart environments which have the aim to help and make easy the life of their inhabitants. In this kind of environments, an important task is the process of activity recognition of an inhabitant in the environment in order to anticipate the occupant necessities and to adapt such smart environment. Due to the cost to checking activity recognition approaches in real environments, usually, they use datasets generated from smart environments. Although there are many datasets for activity recognition in smart environments, it is difficult to find single, interleaved or multioccupancy activity datasets, or combinations of these classes of activities according to the researchers’ needs. In this work, the design and development of a complete dataset with 14 sensors and 9 different activities daily living is described, being this dataset divided into partitions with different classes of activities.

Fuzzy Fog Computing: A Linguistic Approach for Knowledge Inference in Wearable Devices

Fog Computing has emerged as a new paradigm where the processing of data and collaborative services are embedded within smart objects, which cooperate between them to reach common goals. In this work, a rule-based Inference Engine based on fuzzy linguistic approach is integrated in the smart devices. The linguistic representation of local and remote sensors is defined by protoforms, which configure the antecedents of the rules in the Inference Engine. A case study where two inhabitants with a wearable device conduct activities in a Smart Lab is presented. Each wearable device infers the daily activities within the wearable devices by means of the rule-based Inference Engine.

Using Ontologies for the Online Recognition of Activities of Daily Living

The recognition of activities of daily living is an important research area of interest in recent years. The process of activity recognition aims to recognize the actions of one or more people in a smart environment, in which a set of sensors has been deployed. Usually, all the events produced during each activity are taken into account to develop the classification models. However, the instant in which an activity started is unknown in a real environment. Therefore, only the most recent events are usually used. In this paper, we use statistics to determine the most appropriate length of that interval for each type of activity. In addition, we use ontologies to automatically generate features that serve as the input for the supervised learning algorithms that produce the classification model. The features are formed by combining the entities in the ontology, such as concepts and properties. The results obtained show a significant increase in the accuracy of the classification models generated with respect to the classical approach, in which only the state of the sensors is taken into account. Moreover, the results obtained in a simulation of a real environment under an event-based segmentation also show an improvement in most activities.

A Web System for Managing and Monitoring Smart Environments

Smart environments have the ability to record information about the behavior of the people by means of their interactions with the objects within an environment. This kind of environments are providing solutions to address some of the problems associated with the growing size and ageing of the population by means of the recognition of activities, monitoring activities of daily living and adapting the environment. In this contribution, a Web system for managing and monitoring smart environments is introduced as an useful tool to activity recognition. The Web system has the advantages to process the information, accessible services and analytic capabilities. Furthermore, a case study monitored by the proposed Web System is illustrated in order to show its performance, usefulness and effectiveness.

Recognition of Activities in Resource Constrained Environments; Reducing the Computational Complexity

In our current work we propose a strategy to reduce the vast amounts of data produced within smart environments for sensor-based activity recognition through usage of the nearest neighbor (NN) approach. This approach has a number of disadvantages when deployed in resource constrained environments due to its high storage requirements and computational complexity. These requirements are closely related to the size of the data used as input to NN. A wide range of prototype generation (PG) algorithms, which are designed for use with the NN approach, have been proposed in the literature to reduce the size of the data set. In this work, we investigate the use of PG algorithms and their effect on binary sensor-based activity recognition when using a NN approach. To identify the most suitable PG algorithm four datasets were used consisting of binary sensor data and their associated class activities. The results obtained demonstrated the potential of three PG algorithms for sensor-based activity recognition that reduced the computational complexity by up to 95 % with an overall accuracy higher than 90 %.

A Lightweight Distributed Architecture to Integrate Fuzzy Relevant Objects in Real-Time Environments

The development of intelligent environments from scratch means an arduous and complex process. In these environments, the integration of sensors and the design of processing information in real time are key aspects in order to generate feasible solutions. To shed light on this context, in this contribution, we present an architecture for information processing based on object distribution services. The capture and processing of data are developed ubiquitously within mobile devices and ambient computers by means of peer to peer based on fuzzy temporal subscriptions. The main advantage of the use of fuzzy temporal subscriptions is that the information is received by a subscriber when it reaches a desired level of relevance for this subscriber, implying a decrease in the communication burden in the architecture. In order to illustrate the usefulness and effectiveness of our proposal, a scene of an user performing an activity in an intelligent environment is described by means of his interactions with the environmental objects and the identification of users by marker-based tracking.

Fistula renocólica como complicación de radiofrecuencia para tratamiento de carcinoma renal de células claras

Resumen es: Objetivo: Describir un caso clinico de fistula reno colica como complicacion de ablacion por radiofrecuencia de carcinoma de celulas renales. Se revisa l...

Nuevas posibilidades organizativas en la era de la historia clínica electrónica

La historia clínica electrónica (HCE) representa un instrumento de trabajo singular. A los beneficios organizativos más explícitos de su utilización (desaparición de archivos, legibilidad de las anotaciones, inalterabilidad...), se unen otros mucho más trascendentes; entre otros, la plataforma electrónica que subyace a toda HCE posibilita nuevas formas de organización y de prestación de los servicios. En relación con la gestión del conocimiento, la HCE facilita el tránsito desde la historia clínica narrativa tradicional a la historia clínica estructurada, un formato documental mucho más robusto en términos de potencial explotación científica y empresarial. Este documento recoge las iniciativas emprendidas en este Servicio de Urología para extraer el mayor rendimiento de las posibilidades que proporciona un hospital dotado de HCE.