Publication: Implementación de técnicas de bajo consumo en WBSN
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2017
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Abstract
El Internet de las cosas es un paradigma que está revolucionando la manera de interactuar entre las personas y los objetos, dando lugar al manejo de grandes cantidades de información que hay que procesar y que implican un aumento consumo energético en los sistemas y redes de computación que antes no existía, además del impacto ambiental asociado. Esta revolución está afectando a diversos campos, como el de la salud, dando lugar al eHealth. Gracias al eHealth se puede realizar una monitorización continua mediante una red de sensores corporales a enfermos crónicos para realizar una predicción de las crisis sintomáticas. Este estudio se centra en concreto en la migraña, una enfermedad neurológica que afecta entorno al 15 % de la población europea, y cuyo tratamiento da lugar a elevados costes para la administración pública y entidades sanitarias. La predicción de crisis de migraña requiere de una monitorización ambulatoria continua, que genera grandes cantidades de datos que deben ser enviados a un centro de procesado de datos. El dispositivo mediante el cual se recogen estos datos dispone de batería, por tanto es de importante reducir su consumo para aumentar su autonomía. Este estudio se centrará en el análisis de métodos de compresión basados en la transformada de Wavelet, la transformada de Fourier y Compressed Senssing aplicados a las señales biomédicas que requieren de una alta tasa de muestreo (electrocardiograma y fotopletismografía). Los resultados obtenidos muestran que se reducirán hasta un 93 % el volumen de datos a enviar y así conseguir una mayor vida útil de las baterías.
Relation between people and objects faces a revolution due to the paradigm of the Internet of Things. This paradigm leads to the generation of big amounts of information that must be handled, which implies an increase of the energy consumption in the communication networks and computation system unseen few years ago; in addition, there is an environmental impact associated that must be considered as well. This revolution affects several fields such as health that has given rise to the eHealth. Thanks to eHealth the continuous monitoring of patients suffering from a chronic disease can be carried out through the use of a wireless body sensor network, which opens the opportunity to predict symptomatic crisis of these diseases. This study focuses specifically on migraine, a neurological disease that affects 15 % of the European population, and whose treatment leads to high economic costs for the public and private health systems. The prediction of migraine crises requires a continuous and ambulatory monitorization, which generates large amounts of data that must be sent to a data center. The monitoring device uses batteries, so it is important to reduce the energy consumption to increase its autonomy. This study will focus on the analysis of compression methods based on the Wavelet transform, the Fourier transform and Compressed Sensing techniques applied to biomedical signals that require a high sampling rate (electrocardiogram and photoplethysmography). The results show that the amount of data to be sent can be reduced up to 93 % and thus have a longer battery life.
Relation between people and objects faces a revolution due to the paradigm of the Internet of Things. This paradigm leads to the generation of big amounts of information that must be handled, which implies an increase of the energy consumption in the communication networks and computation system unseen few years ago; in addition, there is an environmental impact associated that must be considered as well. This revolution affects several fields such as health that has given rise to the eHealth. Thanks to eHealth the continuous monitoring of patients suffering from a chronic disease can be carried out through the use of a wireless body sensor network, which opens the opportunity to predict symptomatic crisis of these diseases. This study focuses specifically on migraine, a neurological disease that affects 15 % of the European population, and whose treatment leads to high economic costs for the public and private health systems. The prediction of migraine crises requires a continuous and ambulatory monitorization, which generates large amounts of data that must be sent to a data center. The monitoring device uses batteries, so it is important to reduce the energy consumption to increase its autonomy. This study will focus on the analysis of compression methods based on the Wavelet transform, the Fourier transform and Compressed Sensing techniques applied to biomedical signals that require a high sampling rate (electrocardiogram and photoplethysmography). The results show that the amount of data to be sent can be reduced up to 93 % and thus have a longer battery life.
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Trabajo de Fin de Grado en Ingeniería de Computadores (Universidad Complutense, Facultad de Informática, curso 2016/2017)