Conception, design and evaluation of an ICT platform for independent living and remote health monitoring

The current society is dealing with a progressive ageing of the population. Life expectancy is constantly increasing and, at the same time, families tend to have less children than in the past. For these reasons, the global proportion of people aged 60 or over is expected to outnumber the younger age groups. This trend will have a serious impact on the society, since the health related costs will rise, there will be a lack of professional caregivers trained to assist the elderly and more and more people will suffer from chronic diseases that must be treated somehow. To overcome this situation, in the past years many initiatives aiming at increasing the elderly independence were born. The problemin developing technological systems for the elderly is that they are reluctant to try out newsystems and devices, so a great emphasismust be put on the design of an acceptable and usable solution. In this thesis, an ICT platform for independent living of older adults is presented. The platform is based on a standard TV and remote control, in order to lower the risk of technology refusal by older people, and aims at offering a rich set of services that include social networking, support, welfare and health. The health aspect is important but not the leading one, since such platformshould be first perceived as useful for different aspects of their daily life, and not strictly related to the concept the being oldmeans having health problems. Another aimof the proposed platformis to expand the offered services by involving external service providers, that will exploit the basic functionalities offered natively by the platform. The aspects related to the initial studies that let to the definition of system requirements and technical specifications will be presented, together with some preliminary usability results obtained from several user tests. Starting from mid 2016, the proposed platformwill be tested during three field trials in Italy, Belgiumand the Netherlands

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