ADAPT: Approach to Develop context-Aware solutions for Personalised asthma managemenT

The creation of sensors allowing the collection of a high amount of data has been possible thanks to the evolution of information and communication technology. These data must be properly interpreted to deliver meaningful information and services. Context-aware reasoning plays an important role in this task, and it is considered as a hot topic to study in the development of solutions that can be categorised under the scope of Intelligent Environments. This research work studies the use of context-aware reasoning as a tool to provide support in the asthma management process. The contribution of this study is presented as the Approach to Develop context-Aware solutions for Personalised asthma managemenT (ADAPT), which can be used as a guideline to create solutions supporting asthma management in a personalised way. ADAPT proposes context-aware reasoning as an appropriate tool to achieve the personalisation that is required to address the heterogeneity of asthma. This heterogeneity makes people with asthma have different triggers provoking their exacerbations and to experience different symptoms when their exacerbations occur, which is considered as the most challenging characteristic of the condition when it comes to implementing asthma treatments. ADAPT context dimensions are the main contribution of the research work as they directly address the heterogeneity of asthma management by allowing the development of preventive and reactive features that can be customised depending on the characteristics of a person with asthma. The approach also provides support to people not knowing their triggers properly through case-based reasoning, and includes virtual assistant as a complementing technology supporting asthma management. The comprehensive nature of ADAPT motivates the study of the interaction between context-aware reasoning and case-based reasoning in Intelligent Environments, which is also reported as a key contribution of the research work. The inclusion of people with asthma, carers and experts in respiratory conditions in the experiments of the research project was possible to achieve thanks to the collaboration formed with Asthma UK

Modelling Case-Based Reasoning in Situation-Aware Disaster Management

Situation-aware disaster management (SADIM) is a continuous decision-making and action taking process. This process requires prior knowledge of the ongoing environment and context. SADIM comprises two major processes: situation awareness (SA) - a cognitive process that assesses current situations and anticipate future situations in the environment; and, disaster management (DM) which is a decision-making process preventing, preparing, responding, and recovering for and from a disaster. One of the decision-making technologies used in current SADIM is case-based reasoning (CBR) CBR is used for the disaster management element only. Situation awareness process in current SADIM is carried out using domain rules, statistical reasoning and other methods. This paper therefore presents a method of using CBR to carry out both situation assessment and disaster management decision-making processes in SADIM, building on previous work focusing in SA alone. Using CBR for both processes provides the capability of using past experiences to understand the state of the environment and also solve specific disaster problems. The paper evaluates the method through implementation in disaster prevention in the petroleum drilling domain for early kick detection to prevent a blowout disaster. The results show a clear improvement in similarity assessment and problem solving prediction to prevent blowout

Providing home care using context-aware agents

This paper presents an ambient intelligence based architecture model that defines intelligent hybrid agents. These agents have the ability to obtain automatic and real-time information about the context using a set of technologies, such as radio frequency identification, wireless networks and wireless control devices. The architecture can be implemented on a wide diversity of dynamic environments, especially for providing home care to elderly and dependent people

A Framework for Exploiting Internet of Things for Context-Aware Trust-based Personalized Services

In the last years, we have witnessed the introduction of Internet of Things as an integral part of the Internet with billions of interconnected and addressable everyday objects. On the one hand, these objects generate massive volume of data that can be exploited to gain useful insights into our day-to-day needs. On the other hand, context-aware recommender systems (CARSs) are intelligent systems that assist users to make service consumption choices that satisfy their preferences based on their contextual situations. However, one of the major challenges in developing CARSs is the lack of functionality providing dynamic and reliable context information required by the recommendation decision process based on the objects that users interact with in their environments. Thus, contextual information obtained from IoT objects and other sources can be exploited to build CARSs that satisfy users’ preferences, improve quality of experience and recommendation accuracy. This article describes various components of a conceptual IoT based framework for context-aware personalized recommendations. The framework addresses the weakness whereby CARSs rely on static and limited contextual information from user’s mobile phone, by providing additional components for reliable and dynamic contextual information, using IoT context sources. The core of the framework consists of context recognition and reasoning management, dynamic user profile model incorporating trust to improve accuracy of context-aware personalized recommendations. Experimental evaluations show that incorporating context and trust in personalized recommendations can improve its accuracy