Reminder Care System: An Activity-Aware Cross-Device Recommendation System

© 2019, Springer Nature Switzerland AG. Alzheimer’s disease (AD) affects large numbers of elderly people worldwide and represents a significant social and economic burden on society, particularly in relation to the need for long term care facilities. These costs can be reduced by enabling people with AD to live independently at home for a longer time. The use of recommendation systems for the Internet of Things (IoT) in the context of smart homes can contribute to this goal. In this paper, we present the Reminder Care System (RCS), a research prototype of a recommendation system for the IoT for elderly people with cognitive disabilities. RCS exploits daily activities that are captured and learned from IoT devices to provide personalised recommendations. The experimental results indicate that RCS can inform the development of real-world IoT applications

Semantic Smart Homes: Towards Knowledge Rich Assisted Living Environments

International audienceThe complexity of the Emergency Supply Chains makes its management very difficult. Hence, we present in this article a comprehensive view of the French emergency supply chain (ESC), we propose an ad hoc relationship model between actors, and a GRAI grid-based model to initiate a new approach for controlling the ESC deficiencies, especially related to decision making. Throughout the article, we discuss the interest of the use of enterprise modelling to model the ESC. We discuss too, the characterization of the different issues related to the steering of the ESC. A literature review based on the GRAI grid model is proposed and discussed too. The GRAI method is used here because it presents the advantage of using the theory of complex systems, and it provides a dynamic model of an organization by focusing on decision-making and decisions communication

Holistic System Design for Distributed National eHealth Services

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Designing value creating and sustainable business models: An investigation of telehealthcare service ecosystem in North East England

A rising elderly population in England, together with the prevalence of long-term chronic health conditions and higher demands for social care, is creating significant challenges for both the English National Health Service (NHS), and for Local Authorities. These challenges relate to the effective planning, commissioning and provisioning of services for people with complex social and health care needs, amidst a predominantly public-funded health and care system. Digital technology innovations, such as telecare and telehealth (telehealthcare) can facilitate assisted living through technology-mediated preventions, early detections of risks, timely interventions, and self-directed care. Policymakers acknowledge the potential of these technologies to drive greater operational efficiency and cost savings by supporting the policy agenda of ‘ageing in place’, as opposed to an increasing reliance on commissioning expensive institutional provisions such as care homes. In order to realise the opportunities of technology-enabled care, policymakers have started advocating faster adoption, provisioning and implementation of telehealthcare services on an increased population scale. A review of the relevant health technology and systems literature indicates that prior and current research does not sufficiently address the business model and service perspectives, which are considered critical to the practical justification and adoption of complex health service innovations such as telehealthcare. This research study and thesis brings together two interdisciplinary and complementary theoretical frames, synthesised from the extant literature on business models and service innovation. A new theoretical framework is developed in order to examine, interrogate and explain the phenomena of value creation and value realisation within a telehealthcare service ecosystem. Conventional business model-based thinking focuses on value propositions and the financial realisation of value. In contrast, service-dominant logic offers more relational and systemic insights on value co-creation (emphasising social as well as economic factors) through stakeholders’ resource integration within the entire service ecosystem. Using the principles of Critical Realism (CR) to inform a case study approach, this qualitative study employs a multiple case-based research design, resulting in five case studies of telehealthcare services (including one pilot) in the North East of England. The analysis of empirical data collected from the case studies, including a representative sample comprising forty key-informant stakeholder interviews, combined with documentary and observational evidence, reveals four main themes. In the next stage of analysis, following a critical realist perspective, abduction and retroduction based reasoning are applied, leading to a theoretical explanation concerning the underlying structures and their causal powers (mechanisms). Three most significant causal mechanisms, namely Organisational Inertia, Fragmented Ecosystem, and Quasi-market Characteristics, have been identified to explain the stratified reality within a telehealthcare service ecosystem. This research analysis results in both theoretical, and practitioner related contributions concerning the development of a typology for telehealthcare service business models with illustrations of three archetype business models and their related elements. These archetype models signify the dynamic possibilities or potential variations of business models and new service designs contingent upon the operational contexts in which the business models are to be situated

Semantic knowledge base in support of activity recognition in smart home environments

Activity recognition plays a major role in smart home technologies in providing services to users. One of the approaches to identify ac-tivity is through the use of knowledge-driven reasoning. This paper presents a framework of semantic activity recognition, which is used to support smart home systems to identify users’ activities based on the existing context. The framework consists of two main compo-nents: a semantic knowledge base and an activity recognition module. The knowledge base is represented using ontology and it is used to provide a semantic understanding of the environment in order to classify users’ patterns of activities. Experimental results show that the proposed approach can support the classification process and accurately infer users’ activities with the accuracy of 90.9%