Diseño de un sistema de control de riego automatizado aplicado a la viticultura.

La producción de productos agrícolas se ve afectada por desastres medio ambientales visibles en la actualidad, como lo son el calentamiento global, la contaminación o la deforestación. A esto hay que sumarle un mal uso de los recursos hídricos disponibles a nuestro alcance, sobretodo en el caso de grandes áreas de explotación. El clima mediterráneo del que presumía Castilla-La Mancha está al borde de la extinción y, por ello, la forma de cultivo debe volverse más e ciente. Con este proyecto, se propone una nueva forma inteligente de gestionar el riego de plantaciones de medio o gran tamaño. De esta forma, se ha diseñado un sistema de sensores que colaboran con los servicios en la nube para proporcionar al agricultor un asistente en el empleo de agua.Farming production is a ected by environmental disasters visible today, such as global warming, pollution or deforestation. Furthermore, there is a misuse of available water resources, mostly on large production areas. The Mediterranean climate of which Castilla-La Mancha boasted is on the verge of extinction and, therefore, agriculture must become more e cient. Along this project, we propose a new intelligent way of managing the irrigation of medium or large sized areas. A system of sensors has been designed which collaborates with cloud services to provide the farmer an assistant in the use of water

Bedtime Monitoring for Fall Detection and Prevention in Older Adults

Life expectancy has increased, so the number of people in need of intensive care and attention is also growing. Falls are a major problem for older adult health, mainly because of the consequences they entail. Falls are indeed the second leading cause of unintentional death in the world. The impact on privacy, the cost, low performance, or the need to wear uncomfortable devices are the main causes for the lack of widespread solutions for fall detection and prevention. This work present a solution focused on bedtime that addresses all these causes. Bed exit is one of the most critical moments, especially when the person suffers from a cognitive impairment or has mobility problems. For this reason, this work proposes a system that monitors the position in bed in order to identify risk situations as soon as possible. This system is also combined with an automatic fall detection system. Both systems work together, in real time, offering a comprehensive solution to automatic fall detection and prevention, which is low cost and guarantees user privacy. The proposed system was experimentally validated with young adults. Results show that falls can be detected, in real time, with an accuracy of 93.51%, sensitivity of 92.04% and specificity of 95.45%. Furthermore, risk situations, such as transiting from lying on the bed to sitting on the bed side, are recognized with a 96.60% accuracy, and those where the user exits the bed are recognized with a 100% accuracy.This research was funded by H2020 European Union program under grant agreement No. 857159 (SHAPES project) and by MCIN/AEI/10.13039/501100011033 grant TALENT-BELIEF (PID2020-116417RB-C44) and by GoodBrother COST action 19121