With the continued increases in the global population, a serious problem will occur in relation to an ageing population. The reason for this is that a significant number of elderly people may suffer from serial chronic diseases making it difficult for them to live independently and thus they pose a significant economic burden for both themselves and for their government. In order to solve this problem, more and more E-healthcare applications are being designed. In addition, a wide range of medical sensors exist, which can monitor almost all the important physiological parameters of the human body. However, different sensors may use different communication protocols, data units and formats. Additionally, the elderly may use more than one E-healthcare application at the same time and this can lead to problems if there is more than one data source. The project “A Middleware of Wireless sensor networks for Home healthcare monitoring application” is designed to solve these problems.  The focus of this report is on the design and implementation based on multiple modules of data acquisition. There are four data acquisition modules which include the ZigBee module, 6LowPAN module, Wi-Fi module and a Bluetooth module. Thus, multiple data acquisition module middleware supports the sensor by means of ZigBee, 6LowPAN, Wi-Fi and Bluetooth. The tasks of the middleware are to collect packets from sensors, perform data processing and mapping and then displaying as real-time data and finally transmitting the data to the remote server.

Detecting unusual movement (falls) for elderly people in enclosed environments is receiving increasing attention and is likely to have massive potential social and economic impact. In this thesis, new intelligent computer vision processing based techniques are proposed to detect falls in indoor environments for senior citizens living independently, such as in intelligent homes. Different types of features extracted from video-camera recordings are exploited together with both background subtraction analysis and machine learning techniques. Initially, an improved background subtraction method is used to extract the region of a person in the recording of a room environment. A selective updating technique is introduced for adapting the change of the background model to ensure that the human body region will not be absorbed into the background model when it is static for prolonged periods of time. Since two-dimensional features can generate false alarms and are not invariant to different directions, more robust three-dimensional features are next extracted from a three-dimensional person representation formed from video-camera measurements of multiple calibrated video-cameras. The extracted three-dimensional features are applied to construct a single Gaussian model using the maximum likelihood technique. This can be used to distinguish falls from non-fall activity by comparing the model output with a single. In the final works, new fall detection schemes which use only one uncalibrated video-camera are tested in a real elderly person s home environment. These approaches are based on two-dimensional features which describe different human body posture. The extracted features are applied to construct a supervised method for posture classification for abnormal posture detection. Certain rules which are set according to the characteristics of fall activities are lastly used to build a robust fall detection model.

The idea is to determine how game design can be applied to generate and sustain motivation in serious gaming, or applied gaming, design projects. Elements that spark a sense of playfulness embedded in an application in a non-game context have been shown to enhance the user’s engagement. DiabetesNinja is a healthcare app for children with type 1 diabetes, but the children who used it did not engage with it as they were meant to. When living with diabetes, one’s disease-related knowledge and self-management are important, but children can only learn these when they use and interact with the app properly and regularly. Therefore, as a part of learning to live with a chronic disease, the primary aim was to increase children’s motivation to use the application. This study explores relevant theories in the fields of gamification, pedagogy and healthcare. In this exploration, three co-design workshops were conducted to gain an understanding of the children’s current user experience, explore their attitudes towards game elements, and identify design possibilities. The outcome is a serious game design concept where diabetes index registration functions are embedded in the levelling up (bonus) system. In addition, the visually customizable Tamagotchi concept installed in the levelling-up system acts as an assistant to motivate the children on a regular basis and may contribute to the children's long-term engagement. I conclude with how the levelling-up system follows the pattern of diabetes data registration and is an appropriate game mechanic for both motivating children and easing their heavy cognition load in the disease-related index registration sequence.

Computers network in the hospital environments are central topics of discussion on the use of systems applied to health care to ensure data capture of vital signs. This study aims at analyzing the data capture model of the proposed sensor node, using the Modbus protocol communication standard in the acquisition of multi-parametric information of biological signs of patients. The research was conducted with experimental purpose to characterize the ModBus protocol in the RS485 serial network. It was also performed a systematic review to support the choice of the data model and serial communication standard in wired networks. Thus, a sensor node prototype (PIC18F26K20) was built to capture body temperature and heart rate in the wired communication network in which the protocols CAN and ModBUS were tested. The data from the sensor nodes were subjected to capture tests and sending the data to the central node, and displayed on portable platform (Smartphones). It was also observed the bandwidth characteristics and quality of the obtained data. The systematic review showed a trend in the use of the CAN protocol as wired communication standard for HealthCare activities, the application used in the experiment presented limitations. However, after the experiment using the ModBus protocol, this was adequate and easy to implement applications in the hospital environment, having a low-cost platform as a solution to that area. / Redes de computadores em ambientes hospitalares são temas centrais de discussão sobre a utilização de sistemas aplicados à atividade em saúde que garantam a captura dos dados de sinais vitais. O presente trabalho teve como objetivo analisar o modelo de captura dos dados do nodo sensor proposto, utilizando o padrão de comunicação do protocolo ModBUS na aquisição de informações multiparamétricas de sinais biológicos de pacientes. A pesquisa foi desenvolvida com propósito experimental para a caracterização do protocolo ModBUS sob a rede serial RS485. Foi realizada, ainda, uma revisão sistemática para subsidiar a escolha do modelo de dados e do padrão de comunicação serial em redes cabeadas. Para tanto, foi construído um protótipo de nodo sensor (PIC18F26K20) para captura de temperatura corporal e frequência cardíaca na comunicação da rede com fio na qual os protocolos CANbus e ModBUS foram testados. Os dados dos nodos sensores foram submetidos a testes de captura e envio dos dados ao nodo central, e exibidos em plataforma portáteis (Smartphones). Foram, ainda, observadas as características de largura de banda e qualidade dos dados obtidos. A revisão sistemática demonstrou uma tendência na utilização do protocolo CANbus como padrão de comunicação cabeado para as atividades HealthCare, a aplicação utilizada no experimento apresentou limitações. No entanto, após experimento utilizando o protocolo ModBus, este mostrou-se adequado e de fácil implementação em aplicações na área hospitalar, tendo uma plataforma de baixo custo como solução para a referida área.