Examinando por Autor "Medina-Quero, Javier"
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Ítem Fuzzy intelligent system for patients with preeclampsia in wearable devices(Wiley, 2017-10-12) Espinilla-Estévez, Macarena; Medina-Quero, Javier; García-Fernández, Ángel Luis; Campaña, Sixto; Londoño, JorgePreeclampsia 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.Ítem Fuzzy monitoring of in-bed postural changes for the prevention of pressure ulcers using inertial sensors attached to clothing(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2020) Bernal-Monroy, Edna; Polo, Aurora; Espinilla-Estévez, Macarena; Medina-Quero, JavierPostural changes while maintaining a correct body position are the most efficient method of preventing pressure ulcers. However, executing a protocol of postural changes over a long period of time is an arduous task for caregivers. To address this problem, we propose a fuzzy monitoring system for postural changes which recognizes in-bed postures by means of micro inertial sensors attached to patients’ clothes. First, we integrate a data-driven model to classify in-bed postures from the micro inertial sensors which are located in the socks and tshirt of the patient. Second, a knowledge-based fuzzy model computes the priority of postural changes for body zones based on expert-defined protocols. Results show encouraging performance in the classification of in-bed postures and high adaptability of the knowledge-based fuzzy approach.Ítem Intelligent multi-dose medication controller for fever: From wearable devices to remote dispensers(Elsevier, 2018-02-02) Medina-Quero, Javier; Espinilla-Estévez, Macarena; García-Fernández, Ángel-Luis; Martínez-López, LuisIn this work, an Intelligent Medication Controller which analyzes the data streams from body temperature provided by a wearable device is proposed in order to dispense in a low-cost remote dispenser installed at home. The main innovation of our approach is a pharmacokinetic and pharmacodynamic analysis based on the successful fuzzy linguistic approach and fuzzy logic. This analysis provides accuracy and adherence to patient fever in the decision making of medication intakes, adequating the doses and waiting time based on previous intakes. In order to show its efficiency, a case study is presented, in which a complete range of fever episodes is analyzed to compare the decision making in medication intakes of antipyretics between the human expert and the proposed Intelligent Medication Controller. Our proposal has obtained an encouraging performance when recommending medication intakes in a flexible way and assuring a secure response for contraindication cases.Ítem Modelado y Predicción Inmediata de Potencia Fotovoltaica Mediante Técnicas de Internet de las Cosas, Aprendizaje Automático y Redes Neuronales(2024-10-03) Almonacid-Olleros, Guillermo; Medina-Quero, Javier; Almonacid-Puche, Gabino; Universidad de Jaén. Departamento de InformáticaEsta tesis investiga y aplica tecnologías de vanguardia en energía solar fotovoltaica, enfocándose en el análisis y predicción de la generación de energía eléctrica.Dado el creciente interés en la energía sostenible, se desarrollaron técnicas avanzadas que incluyen el Internet de las Cosas (IoT), el Aprendizaje Profundo (Deep Learning) y las Redes Generativas Adversarias (GANs). Se creó un sistema de adquisición de datos inalámbrico utilizando sensores IoT para monitorizar sistemas fotovoltaicos, mejorando a eficiencia y estandarización de la captura de datos. El uso de modelos de aprendizaje profundo, como LSTM y CNNs, permitió mejorar la precisión en la predicción de energía. Además, se exploró el aprendizaje por transferencia y el uso de GANs para generar datos sintéticos, mejorando la robustez y generalización de los modelos. Estos enfoques innovadores han demostrado ser efectivos para optimizar la gestión y predicción en sistemas fotovoltaicos. This thesis investigates and applies cutting-edge technologies in photovoltaic solar energy, focusing on the analysis and prediction of electricity generation. Given the growing interest in sustainable energy, advanced techniques were developed, including the Internet of Things (IoT), Deep Learning, and Generative Adversarial Networks (GANs). A wireless data acquisition system was created using IoT sensors to monitor photovoltaic systems, improving the efficiency and standardization of data capture. The use of deep learning models, such as LSTM and CNNs, enhanced the accuracy of energy prediction. Additionally, transfer learning and GANs were explored to generate synthetic data, improving the robustness and generalization of the models. These innovative approaches have proven effective in optimizing the management and prediction of photovoltaic systems.Ítem Modelado y Predicción Inmediata de Potencia Fotovoltaica Mediante Técnicas de Internet de las Cosas, Aprendizaje Automático y Redes Neuronales(2024-10-03) Almonacid-Olleros, Guillermo; Medina-Quero, Javier; Almonacid-Puche, Gabino; Universidad de Jaén. Departamento de InformáticaEsta tesis investiga y aplica tecnologías de vanguardia en energía solar fotovoltaica, enfocándose en el análisis y predicción de la generación de energía eléctrica. Dado el creciente interés en la energía sostenible, se desarrollaron técnicas avanzadas que incluyen el Internet de las Cosas (IoT), el Aprendizaje Profundo (Deep Learning) y las Redes Generativas Adversarias (GANs). Se creó un sistema de adquisición de datos inalámbrico utilizando sensores IoT para monitorizar sistemas fotovoltaicos, mejorando a eficiencia y estandarización de la captura de datos. El uso de modelos de aprendizaje profundo, como LSTM y CNNs, permitió mejorar la precisión en la predicción de energía. Además, se exploró el aprendizaje por transferencia y el uso de GANs para generar datos sintéticos, mejorando la robustez y generalización de los modelos. Estos enfoques innovadores han demostrado ser efectivos para optimizar la gestión y predicción en sistemas fotovoltaicos This thesis investigates and applies cutting-edge technologies in photovoltaic solar energy, focusing on the analysis and prediction of electricity generation. Given the growing interest in sustainable energy, advanced techniques were developed, including the Internet of Things (IoT), Deep Learning, and Generative Adversarial Networks (GANs). A wireless data acquisition system was created using IoT sensors to monitor photovoltaic systems, improving the efficiency and standardization of data capture. The use of deep learning models, such as LSTM and CNNs, enhanced the accuracy of energy prediction. Additionally, transfer learning and GANs were explored to generate synthetic data, improving the robustness and generalization of the models. These innovative approaches have proven effective in optimizing the management and prediction of photovoltaic systems.Ítem Monwatch: A fuzzy application to monitor the user behavior using wearable trackers(IEEE, 2020-07) Martínez-Cruz, Carmen; Medina-Quero, Javier; Serrano, José M.; Gramajo, SergioNowadays, the proliferation of wearable devices has enabled monitoring user behaviours and activities in a non-invasive, autonomous and straightforward way. Moreover, new trend analysis has been boosted by biosignal sensors from wearable trackers, such as inertial or heart rate sensors. The knowledge of such user activity presents a personalized monitoring to prevent any kind of physical or neurological disorders through the sensor evaluation by an expert. To this end, the definition of key indicators and the display of results and relevant analyses require of agile and effective tools. Therefore, this proposal presents a novel web application where the data obtained from a Fitbit Ionic smartwatch wearable are collected, synchronized and compiled to present a summary of an user’s daily activity, which is defined by a linguistic description using fuzzy logic to represent the most relevant Health Key Indicators (HKI). Moreover, an analysis of the user’s behaviour over time is proposed by inferring relevant patterns from a fuzzy clustering algorithm.Ítem Nuevas metodologías para el reconocimiento de cambios posturales a través de sensores(Jaén : Universidad de Jaén, 2021-11-19) Bernal-Monroy, Edna; Espinilla-Estévez, Macarena; Medina-Quero, Javier; Universidad de Jaén. Departamento de InformáticaCon el fin de posibilitar nuevas alternativas que permitan mitigar la complicación de las úlceras por presión, en este trabajo se presentan los resultados de investigación de la tesis doctoral, que han permitido implementar dos metodologías de reconocimiento de cambios posturales de monitoreo en tiempo real, con dispositivos vestibles inerciales no invasivos para la detección y cálculo de postura, usando técnicas de inteligencia artificial. La primera metodología está basada en un registro histórico de la actividad corporal, dataset, y por el reconocimiento de posturas en tiempo real con técnicas de Inteligencia Artificial. Por su parte, la segunda metodología comprende el uso de dispositivos vestibles inerciales en zonas no invasivas, encargados de registrar el tiempo en que la persona ha permanecido en la misma posición, la recolección de datos de personas reales en diferentes posturas, la estimación de las posturas en tiempo real se realiza mediante técnicas de inteligencia artificial.