Designing community care systems with AUML

This paper describes an approach to developing an appropriate agent environment appropriate for use in community care applications. Key to its success is that software designers collaborate with environment builders to provide the levels of cooperation and support required within an integrated agent–oriented community system. Agent-oriented Unified Modeling Language (AUML) is a practical approach to the analysis, design, implementation and management of such an agent-based system, whilst providing the power and expressiveness necessary to support the specification, design and organization of a health care service. The background of an agent-based community care application to support the elderly is described. Our approach to building agent–oriented software development solutions emphasizes the importance of AUML as a fundamental initial step in producing more general agent–based architectures. This approach aims to present an effective methodology for an agent software development process using a service oriented approach, by addressing the agent decomposition, abstraction, and organization characteristics, whilst reducing its complexity by exploiting AUML’s productivity potential. </p

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

CoachAI: A Conversational Agent Assisted Health Coaching Platform

Poor lifestyle represents a health risk factor and is the leading cause of morbidity and chronic conditions. The impact of poor lifestyle can be significantly altered by individual behavior change. Although the current shift in healthcare towards a long lasting modifiable behavior, however, with increasing caregiver workload and individuals' continuous needs of care, there is a need to ease caregiver's work while ensuring continuous interaction with users. This paper describes the design and validation of CoachAI, a conversational agent assisted health coaching system to support health intervention delivery to individuals and groups. CoachAI instantiates a text based healthcare chatbot system that bridges the remote human coach and the users. This research provides three main contributions to the preventive healthcare and healthy lifestyle promotion: (1) it presents the conversational agent to aid the caregiver; (2) it aims to decrease caregiver's workload and enhance care given to users, by handling (automating) repetitive caregiver tasks; and (3) it presents a domain independent mobile health conversational agent for health intervention delivery. We will discuss our approach and analyze the results of a one month validation study on physical activity, healthy diet and stress management

Towards a Smarter organization for a Self-servicing Society

Traditional social organizations such as those for the management of healthcare are the result of designs that matched well with an operational context considerably different from the one we are experiencing today. The new context reveals all the fragility of our societies. In this paper, a platform is introduced by combining social-oriented communities and complex-event processing concepts: SELFSERV. Its aim is to complement the "old recipes" with smarter forms of social organization based on the self-service paradigm and by exploring culture-specific aspects and technological challenges.Comment: Final version of a paper published in the Proceedings of International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI'16), special track on Emergent Technologies for Ambient Assisted Living (ETAAL

Safeguarding health data with enhanced accountability and patient awareness

Several factors are driving the transition from paper-based health records to electronic health record systems. In the United States, the adoption rate of electronic health record systems significantly increased after "Meaningful Use" incentive program was started in 2009. While increased use of electronic health record systems could improve the efficiency and quality of healthcare services, it can also lead to a number of security and privacy issues, such as identity theft and healthcare fraud. Such incidents could have negative impact on trustworthiness of electronic health record technology itself and thereby could limit its benefits. In this dissertation, we tackle three challenges that we believe are important to improve the security and privacy in electronic health record systems. Our approach is based on an analysis of real-world incidents, namely theft and misuse of patient identity, unauthorized usage and update of electronic health records, and threats from insiders in healthcare organizations. Our contributions include design and development of a user-centric monitoring agent system that works on behalf of a patient (i.e., an end user) and securely monitors usage of the patient's identity credentials as well as access to her electronic health records. Such a monitoring agent can enhance patient's awareness and control and improve accountability for health records even in a distributed, multi-domain environment, which is typical in an e-healthcare setting. This will reduce the risk and loss caused by misuse of stolen data. In addition to the solution from a patient's perspective, we also propose a secure system architecture that can be used in healthcare organizations to enable robust auditing and management over client devices. This helps us further enhance patients' confidence in secure use of their health data.PhDCommittee Chair: Mustaque Ahamad; Committee Member: Douglas M. Blough; Committee Member: Ling Liu; Committee Member: Mark Braunstein; Committee Member: Wenke Le

A multi-agent system for pervasive healthcare

Wireless technology based pervasive healthcare has been proposed in many applications such as disease management and accident prevention for cost saving and promoting citizen’s wellbeing. However, the emphasis so far is on the artefacts with limited attentions to guiding the development of an effective and efficient solution for pervasive healthcare. Therefore, this paper aims to propose a framework of multi-agent systems design for pervasive healthcare by adopting the concept of pervasive informatics and using the methods of organisational semiotics. The proposed multi-agent system for pervasive healthcare utilises sensory information to support healthcare professionals for providing appropriate care. The key contributions contain theoretical aspect and practical aspect. In theory, this paper articulates the information interactions between the pervasive healthcare environment and stakeholders by using the methods of organisational semiotics; in practice, the proposed framework improves the healthcare quality by providing appropriate medical attentions when and as needed. In this paper, both systems and functional architecture of the multi-agent system are elaborated with the use of wireless technologies such as RFID and wireless sensor networks. The future study will focus on the implementation of the proposed framework

A patient agent controlled customized blockchain based framework for internet of things

Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph