A position paper on predicting the onset of nocturnal enuresis using advanced machine learning

Bed-wetting during normal sleep in children and young people has a significant impact on the child and their parents. The condition is known as nocturnal enuresis and its underlying cause has been subject to different explanatory factors that include, neurological, urological, sleep, genetic and psychosocial influences. Several clinical and technological interventions for managing nocturnal enuresis exist that include the clinician’s opinions, pharmacology interventions, and alarm systems. However, most have failed to produce any convincing results. Clinical information is often subjective and often inaccurate, the use of desmopressin and tricyclic antidepressants only report between 20 % and 40 % success, and alarms only a 50 % success fate. This position paper posits an alternative research idea concerned with the early detection of impending involuntary bladder release. The proposed framework is a measurement and prediction system that processes moisture and bladder volume data from sensors fitted into undergarments that are used by patients suffering with nocturnal enuresis. The proposed framework represents a level of sophistication in nocturnal enuresis treatment not previously considered

A Smart Framework for Predicting the Onset of Nocturnal Enuresis (PrONE) in Children and Young People

Bed wetting during normal sleep in children and young people has a significant impact on the child and their parents. The condition is known as nocturnal enuresis and its underlying cause has been subject to different explanatory factors that include, neurological, urological, sleep, genetic and psychosocial influences. Several clinical and technological interventions for managing nocturnal enuresis exist that include the clinician’s opinions, pharmacology interventions, and alarm systems. However, most have failed to produce any convincing results; clinical information is often subjective and often inaccurate, the use of desmopression and tricyclic antidepressants only report between 20% and 40% success, and alarms only a 50% success fate. This paper posits an alternative research idea concerned with the early detection of impending involuntary bladder release. The proposed framework is a measurement and prediction system that processes moisture and bladder volume data from sensors fitted into undergarments that are used by patients suffering with nocturnal enuresis. The proposed framework represents a level of sophistication and accuracy in nocturnal enuresis treatment not previously considered

Battery-less near field communications (nfc) sensors for internet of things (iot) applications

L’ implementació de la tecnologia de comunicació de camp proper (NFC) en els telèfons intel·ligents no para de créixer degut a l’ús d’aquesta per fer pagaments, això, junt amb el fet de poder aprofitar l’energia generada pel mòbil no només per la comunicació, sinó també per transmetre energia, el baix cost dels xips NFC, i el fet de que els telèfons tinguin connectivitat amb internet, possibilita i fa molt interesant el disseny d’etiquetes sense bateria incorporant-hi sensors i poder enviar la informació al núvol, dins del creixent escenari de l’internet de les coses (IoT). La present Tesi estudia la viabilitat d’aquests sensors, analitzant la màxima distància entre lector i sensor per proveir la potència necessària, presenta tècniques per augmentar el rang d’operació, i analitza els efectes de certs materials quan aquests estan propers a les antenes. Diversos sensors han estat dissenyats i analitzats i son presentats en aquest treball. Aquests son: Una etiqueta que mesura la humitat de la terra, la temperatura i la humitat relativa de l’aire per controlar les condicions de plantes. Un sensor per detectar la humitat en bolquers, imprès en material flexible que s’adapta a la forma del bolquer. Dues aplicacions, una per estimació de pH i una altre per avaluar el grau de maduració de fruites, basats en un sensor de color. I, per últim, s’estudia la viabilitat de sensors en implants per aplicacions mèdiques, analitzant l’efecte del cos i proposant un sistema per augmentar la profunditat a la que aquests es poden llegir utilitzant un telèfon mòbil. Tots aquests sensors poden ser alimentats i llegits per qualsevol dispositiu que disposin de connexió NFC.La implementación de la tecnología de comunicaciones de campo cercano (NFC) en los teléfonos inteligentes no para de crecer debido al uso de esta para llevar a cabo pagos, esto, junto con el hecho de poder aprovechar la energía generada por el móvil no sólo para la comunicación, sino también para transmitir energía, el bajo coste de los chips NFC, i el hecho que los teléfonos tengan conectividad a internet, posibilita y hace muy interesante el diseño de etiquetas sin batería que incorporen sensores i poder enviar la información a la nube, enmarcado en el creciente escenario del internet de las cosas (IoT). La presente Tesis estudia la viabilidad de estos sensores, analizando la máxima distancia entre lector i sensor para proveer la potencia necesaria, presenta técnicas para aumentar el rango de operación, y analiza los efectos de ciertos materiales cuando estos están cerca de las antenas. Varios sensores han sido diseñados y analizados y son presentados en este trabajo. Estos son: Una etiqueta que mide la humedad de la tierra, la temperatura y la humedad relativa del aire para controlar las condiciones de plantas. Un sensor para detectar la humedad en pañales, impreso en material flexible que se adapta a la forma del pañal. Dos aplicaciones, una para estimación de pH y otra para evaluar el grado de maduración de frutas, basados en un sensor de color. Y, por último, se estudia la viabilidad de sensores en implantes para aplicaciones médicas, analizando el efecto del cuerpo y proponiendo un sistema para aumentar la profundidad a la que estos se pueden leer usando un teléfono móvil. Todos estos sensores pueden ser alimentados y leídos por cualquier dispositivo que disponga de conexión NFC.The implementation of near field communication (NFC) technology into smartphones grows rapidly due the use of this technology as a payment system. This, altogether with the fact that the energy generated by the phone can be used not only to communicate but for power transfer as well, the low-cost of the NFC chips, and the fact that the smartphones have connectivity to internet, makes possible and very interesting the design of battery-less sensing tags which information can be sent to the cloud, within the growing internet of things (IoT) scenario. This Thesis studies the feasibility of these sensors, analysing the maximum distance between reader and sensor to provide the necessary power, presents techniques to increase the range of operation, and analyses the effects of certain materials when they are near to the antennas. Several sensors have been designed and analysed and are presented in this work. These are: a tag that measures the soil moisture, the temperature and the relative humidity of the air to control the conditions of plants. A moisture sensor for diapers, printed on flexible material that adapts to the diaper shape. Two applications, one for pH estimation and another for assessing the degree of fruit ripening, based on a colour sensor. And finally, the feasibility of sensors in implants for medical applications is studied, analysing the effect of the body and proposing a system to increase the depth at which they can be read using a mobile phone. All of these sensors can be powered and read by any NFC enabled device

Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months