The Ethical Implications of Personal Health Monitoring

Personal Health Monitoring (PHM) uses electronic devices which monitor and record health-related data outside a hospital, usually within the home. This paper examines the ethical issues raised by PHM. Eight themes describing the ethical implications of PHM are identified through a review of 68 academic articles concerning PHM. The identified themes include privacy, autonomy, obtrusiveness and visibility, stigma and identity, medicalisation, social isolation, delivery of care, and safety and technological need. The issues around each of these are discussed. The system / lifeworld perspective of Habermas is applied to develop an understanding of the role of PHMs as mediators of communication between the institutional and the domestic environment. Furthermore, links are established between the ethical issues to demonstrate that the ethics of PHM involves a complex network of ethical interactions. The paper extends the discussion of the critical effect PHMs have on the patient’s identity and concludes that a holistic understanding of the ethical issues surrounding PHMs will help both researchers and practitioners in developing effective PHM implementations

Physical activity recognition by utilising smartphone sensor signals

Human physical motion activity identification has many potential applications in various fields, such as medical diagnosis, military sensing, sports analysis, and human-computer security interaction. With the recent advances in smartphones and wearable technologies, it has become common for such devices to have embedded motion sensors that are able to sense even small body movements. This study collected human activity data from 60 participants across two different days for a total of six activities recorded by gyroscope and accelerometer sensors in a modern smartphone. The paper investigates to what extent different activities can be identified by utilising machine learning algorithms using approaches such as majority algorithmic voting. More analyses are also provided that reveal which time and frequency domain-based features were best able to identify individuals’ motion activity types. Overall, the proposed approach achieved a classification accuracy of 98% in identifying four different activities: walking, walking upstairs, walking downstairs, and sitting (on a chair) while the subject is calm and doing a typical desk-based activity

Physical activity recognition by utilising smartphone sensor signals

Human physical motion activity identification has many potential applications in various fields, such as medical diagnosis, military sensing, sports analysis, and human-computer security interaction. With the recent advances in smartphones and wearable technologies, it has become common for such devices to have embedded motion sensors that are able to sense even small body movements. This study collected human activity data from 60 participants across two different days for a total of six activities recorded by gyroscope and accelerometer sensors in a modern smartphone. The paper investigates to what extent different activities can be identified by utilising machine learning algorithms using approaches such as majority algorithmic voting. More analyses are also provided that reveal which time and frequency domain-based features were best able to identify individuals' motion activity types. Overall, the proposed approach achieved a classification accuracy of 98% in identifying four different activities: walking, walking upstairs, walking downstairs, and sitting (on a chair) while the subject is calm and doing a typical desk-based activity

Real-World Smartphone-based Gait Recognition

As the smartphone and the services it provides are becoming targets of cybercrime, it is critical to secure smartphones. However, it is important security controls are designed to provide continuous and user-friendly security. Amongst the most important of these is user authentication, where users have experienced a significant rise in the need to authenticate to the device and individually to the numerous apps that it contains. Gait authentication has gained attention as a mean of non-intrusive or transparent authentication on mobile devices, capturing the information required to verify the authenticity of the user whilst the person is walking. Whilst prior research in this field has shown promise with good levels of recognition performance, the results are constrained by the gait datasets utilised being based upon highly controlled laboratory-based experiments which lack the variability of real-life environments. This paper introduces an advanced real-world smartphone-based gait recognition system that recognises the subject within real-world unconstrained environments. The proposed model is applied to the uncontrolled gait dataset, which consists of 44 users over a 7–10 day capture – where users were merely asked to go about their daily activities. No conditions, controls or expectations of particular activities were placed upon the participants. The experiment has modelled four types of motion normal walking, fast walking and down and upstairs for each of the users. The evaluation of the proposed model has achieved an equal error rate of 11.38%, 11.32%, 24.52%, 27.33% and 15.08% for the normal, fast, down and upstairs and all activities respectively. The results illustrate, within an appropriate framework, that gait recognition is a viable technique for real-world use

FollowThem: aplicação móvel para monitorizar crianças e idosos

Currently, there is a growing interest in developing mobile applications in an attempt to overcome loneliness, especially on the senior age group. This interest also extends to children monitoring. In this context, a mobile application developed for the Android operating system, designated FollowThem, is presented. Besides the mobile application, two other components were developed: a caregiver area and an information area. This mobile application is designed to monitor and assist elderly and children, in order to allow them to feel safer, while simultaneously providing support to their caregivers. FollowThem has an intuitive interface and offers useful features to their users, such as detecting their geographical position, proximity and falls, among other features not less important, that will be described throughout the work. The main difference between FollowThem and other applications residing on the market of mobile applications is the meeting of various dispersed functionalities as well as the introduction of several innovative functionalities. This work is guided mostly to describe the mobile application since this is the main contribution of this work. Its most important functionalities are described. The following components are also presented: a caregiver area, which is designed to provide give support to caregivers; and an information area, which is designed to clarify in what the system consists.Atualmente verifica-se um interesse crescente no desenvolvimento de aplicações móveis numa tentativa de contornar a solidão existente na faixa etária sénior. Este interesse estendese, igualmente, aos pais na educação dos seus filhos, no que concerne aos cuidados de vigilância que estas aplicações poderão permitir prestar. Neste contexto, é apresentada uma aplicação móvel desenvolvida para o sistema operativo Android, designada FollowThem. Além da aplicação móvel foram desenvolvidos dois outros componentes: área de cuidador e uma área informativa. A presente aplicação móvel tem como objetivo monitorizar e ajudar idosos e crianças, de forma a se sentirem mais seguros e, simultaneamente dar apoio aos seus cuidadores. FollowThem tem uma interface intuitiva e oferece funcionalidades úteis para os seus utilizadores como detetar a sua posição geográfica, proximidade e quedas, entre outras funcionalidades não menos importantes, referidas ao longo deste trabalho. A principal diferença entre a aplicação FollowThem e as restantes aplicações, residentes no mercado de aplicações móveis, é a reunião de diversas funcionalidades atualmente dispersas por várias aplicações, como também a introdução de funções inovadoras. Este trabalho de mestrado orienta-se, maioritariamente, para a aplicação móvel, uma vez que esta é a principal contribuição. São descritas as suas funcionalidades mais relevantes, sendo, também, apresentados os seguintes componentes: área de cuidador, desenvolvida para dar apoio aos cuidadores, e a área informativa, desenvolvida para esclarecer, de uma forma geral, em que consiste o sistema

Modelling and Predicting Energy Usage from Smart Meter Data and Consumer behaviours in Residential Houses.

Efforts of electrical utilities to respond to climate change requires the development of increasingly sophisticated, integrated electrical grids referred to as the smart grids. Much of the smart grid effort focuses on the integration of renewable generation into the electricity grid and on increased monitoring and automation of electrical transmission functions. However, a key component of smart grid development is the introduction of the smart electrical meter for all residential electrical customers. Smart meter deployment is the corner stone of the smart grid. In addition to adding new functionality to support system reliability, smart meters provide the technological means for utilities to institute new programs to allow their customers to better manage and reduce their electricity use and to support increased renewable generation to reduce greenhouse emissions from electricity use. As such, this thesis presents our research towards the study of how the data (energy usage profiles) produced by the smart meters within the smart grid system of residential homes is used to profile energy usage in homes and detect users with high fuel consumption levels. This project concerns the use of advanced machine learning algorithms to model and predict household behaviour patterns from smart meter readings. The aim is to learn and understand the behavioural trends in homes (as demonstrated in chapter 5). The thesis shows the trends of how energy is used in residential homes. By obtaining these behavioural trends, it is possible for utility companies to come up with incentives that can be beneficial to home users on changes that can be adopted to reduce their carbon emissions. For example consumers would be more likely prompted to turn of unusable appliances that are consuming high energy around the home e.g., lighting in rooms which are un occupied. The data used for the research is constructed from a digital simulation model of a smart home environment comprised of 5 residential houses. The model can capture data from this simulated network of houses, hence providing an abundance set of information for utility companies and data scientist to promote reductions in energy usage. The simulation model produces volumes of outliers such as high periods (peak hours) of energy usage and low periods (Off peak hours) of anomalous energy consumption within the residential setting of five homes. To achieve this, performance characteristics on a dataset comprised of wealthy data readings from 5 homes is analysed using Area under ROC Curve (AUC), Precision, F1 score, Accuracy and Recall. The highest result is achieved using the Two-Class Decision Forest classifier, which achieved 87.6% AUC