The future of social is personal: the potential of the personal data store

This chapter argues that technical architectures that facilitate the longitudinal, decentralised and individual-centric personal collection and curation of data will be an important, but partial, response to the pressing problem of the autonomy of the data subject, and the asymmetry of power between the subject and large scale service providers/data consumers. Towards framing the scope and role of such Personal Data Stores (PDSes), the legalistic notion of personal data is examined, and it is argued that a more inclusive, intuitive notion expresses more accurately what individuals require in order to preserve their autonomy in a data-driven world of large aggregators. Six challenges towards realising the PDS vision are set out: the requirement to store data for long periods; the difficulties of managing data for individuals; the need to reconsider the regulatory basis for third-party access to data; the need to comply with international data handling standards; the need to integrate privacy-enhancing technologies; and the need to future-proof data gathering against the evolution of social norms. The open experimental PDS platform INDX is introduced and described, as a means of beginning to address at least some of these six challenges

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Empowerment or Engagement? Digital Health Technologies for Mental Healthcare

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare

Elena+ Care for COVID-19, a Pandemic Lifestyle Care Intervention: Intervention Design and Study Protocol

Background: The current COVID-19 coronavirus pandemic is an emergency on a global scale, with huge swathes of the population required to remain indoors for prolonged periods to tackle the virus. In this new context, individuals\u27 health-promoting routines are under greater strain, contributing to poorer mental and physical health. Additionally, individuals are required to keep up to date with latest health guidelines about the virus, which may be confusing in an age of social-media disinformation and shifting guidelines. To tackle these factors, we developed Elena+, a smartphone-based and conversational agent (CA) delivered pandemic lifestyle care intervention. Methods: Elena+ utilizes varied intervention components to deliver a psychoeducation-focused coaching program on the topics of: COVID-19 information, physical activity, mental health (anxiety, loneliness, mental resources), sleep and diet and nutrition. Over 43 subtopics, a CA guides individuals through content and tracks progress over time, such as changes in health outcome assessments per topic, alongside user-set behavioral intentions and user-reported actual behaviors. Ratings of the usage experience, social demographics and the user profile are also captured. Elena+ is available for public download on iOS and Android devices in English, European Spanish and Latin American Spanish with future languages and launch countries planned, and no limits on planned recruitment. Panel data methods will be used to track user progress over time in subsequent analyses. The Elena+ intervention is open-source under the Apache 2 license (MobileCoach software) and the Creative Commons 4.0 license CC BY-NC-SA (intervention logic and content), allowing future collaborations; such as cultural adaptions, integration of new sensor-related features or the development of new topics. Discussion: Digital health applications offer a low-cost and scalable route to meet challenges to public health. As Elena+ was developed by an international and interdisciplinary team in a short time frame to meet the COVID-19 pandemic, empirical data are required to discern how effective such solutions can be in meeting real world, emergent health crises. Additionally, clustering Elena+ users based on characteristics and usage behaviors could help public health practitioners understand how population-level digital health interventions can reach at-risk and sub-populations

Assessment of Domestic Well-Being: From Perception to Measurement

Nowadays, there are plenty of sensing devices that enable the measurement of physiological, environmental, and behavioral parameters of people 24 hours a day, seven days a week and provide huge quantities of different data. Data and signals coming from sensing devices, installed in indoor or outdoor environments or often worn by the users, generate heterogeneous and complex structured datasets, most of the time not uniformly structured. The artificial intelligence (AI) algorithms applied to these sets of data have demonstrated capabilities to infer indices related to a subject's status and well-being [1]. Well-being is a key parameter in the World Health Organization (WHO) definition of health, considering its physical, mental, and social spheres. Quantitatively assessing a subject's well-being is of paramount importance if we want to assess the whole status of a person, which is particularly useful in the case of ageing people living alone. Assessment allows for continuous remote monitoring to improve people's quality of life (QoL) according to their perceptions, needs, and preferences. Technology undoubtedly plays a pivotal role in this regard, providing us new tools to support the objective evaluation of a subject's status, including her/his perception of the living environment. Its potential is huge, also in terms of support to the healthcare system and ageing people; however, there are several engineering challenges to consider, especially in terms of sensors integrability, connectivity, and metrological performance, in order to obtain reliable and accurate measurement systems