A Unified Recommendation Framework for Data-driven, People-centric Smart Home Applications

With the rapid growth in the number of things that can be connected to the internet, Recommendation Systems for the IoT (RSIoT) have become more significant in helping a variety of applications to meet user preferences, and such applications can be smart home, smart tourism, smart parking, m-health and so on. In this thesis, we propose a unified recommendation framework for data-driven, people-centric smart home applications. The framework involves three main stages: complex activity detection, constructing recommendations in timely manner, and insuring the data integrity. First, we review the latest state-of-the-art recommendations methods and development of applications for recommender system in the IoT so, as to form an overview of the current research progress. Challenges of using IoT for recommendation systems are introduced and explained. A reference framework to compare the existing studies and guide future research and practices is provided. In order to meet the requirements of complex activity detection that helps our system to understand what activity or activities our user is undertaking in relatively high level. We provide adequate resources to be fit for the recommender system. Furthermore, we consider two inherent challenges of RSIoT, that is, capturing dynamicity patterns of human activities and system update without a focus on user feedback. Based on these, we design a Reminder Care System (RCS) which harnesses the advantages of deep reinforcement learning (DQN) to further address these challenges. Then we utilize a contextual bandit approach for improving the quality of recommendations by considering the context as an input. We aim to address not only the two previous challenges of RSIoT but also to learn the best action in different scenarios and treat each state independently. Last but not least, we utilize a blockchain technology to ensure the safety of data storage in addition to decentralized feature. In the last part, we discuss a few open issues and provide some insights for future directions

Constructing the mathematical model of a recommender system for decentralized peer-to-peer computer networks

Recommender systems make it easier to search with a large amount of content, supplementing or replacing the classic search output with recommendations. In P2P networks, their use can have additional benefits. Because of indexing and search problems, previously added files may not be available to P2P network users. If the user cannot find the file he is looking for, one can provide him with a list of recommendations based on his preferences and search query. The object of research is the process of creating recommendations for users of decentralized P2P networks to facilitate data search. The urgent task of increasing the accuracy of mathematical modeling of recommender systems by taking into account the requirements for reliability and data security during changes in the structure of a decentralized P2P network is solved. An analytical model of the recommender system of a decentralized P2P network has been developed, the main feature of which is taking into account the requirements of reliability and security of recommendation messages. This was done by introducing the following indicators into the general model of the decentralized recommender system – the probability of reliable packet transmission and the probability of safe packet transmission. The developed analytical model makes it possible to conduct a comparative analysis of different methods of operation of recommender systems and to set acceptable parameters under which the degree of relevance does not fall below a certain threshold. The developed mathematical model of the system based on the GERT scheme differs from the known ones by taking into account the reliability and security requirements during changes in the structure of the decentralized P2P network. This has made it possible to improve the accuracy of simulation results up to 5 %. The proposed mathematical model could be used for prototyping recommender systems in various fields of activit

Towards a self adaptive system for social wellness

Advancements in science and technology have highlighted the importance of robust healthcare services, lifestyle services and personalized recommendations. For this purpose patient daily life activity recognition, profile information, and patient personal experience are required. In this research work we focus on the improvement in general health and life status of the elderly through the use of an innovative services to align dietary intake with daily life and health activity information. Dynamic provisioning of personalized healthcare and life-care services are based on the patient daily life activities recognized using smart phone. To achieve this, an ontology-based approach is proposed, where all the daily life activities and patient profile information are modeled in ontology. Then the semantic context is exploited with an inference mechanism that enables fine-grained situation analysis for personalized service recommendations. A generic system architecture is proposed that facilitates context information storage and exchange, profile information, and the newly recognized activities. The system exploits the patient’s situation using semantic inference and provides recommendations for appropriate nutrition and activity related services. The proposed system is extensively evaluated for the claims and for its dynamic nature. The experimental results are very encouraging and have shown better accuracy than the existing system. The proposed system has also performed better in terms of the system support for a dynamic knowledge-base and the personalized recommendations

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions

EAST MIDLANDS INTEGRATED LIFESTYLE (ILS) DATABASE: FEASIBILITY STUDY - FINAL REPORT

EXECUTIVE SUMMARY Background: A regional integrated database could serve as a rich data source for in-depth analysis in research studies across key Public Health lifestyle areas in the East Midlands. This could inform Public Health policy, service delivery and commissioning decisions. Unfortunately, existing datasets are poorly aligned across the four key Public Health lifestyle areas examined in this study: physical activity, smoking cessation, reduction in alcohol consumption, and diet and weight management. This feasibility study was therefore commissioned by the East Midlands Directors of Public Health Group chaired by Professor Derek Ward, Director of Public Health in Lincolnshire, with funding from the NIHR East Midlands Clinical Research Network and the College of Social Science, University of Lincoln. Public Health researchers in the Mental Health, Health and Social Care Research Group (MH2aSC) at the University of Lincoln were invited to carry out the study to explore the feasibility of developing and implementing an integrated lifestyle database across the East Midlands Region. Methods: A scoping review for available evidence was conducted to inform decisions about feasibility of the proposed integrated lifestyle database. This was followed by a consultation exercise with 18 stakeholders, predominantly in the East Midlands, from September 2020 to February 2021. The consultation exercise sought to gather the views of stakeholders, purposively invited to take part due to their role in public health, about the potential feasibility of an integrated database. Stakeholders were identified and invited by email to participate in the consultation meetings which took place via Microsoft Teams. A topic guide, designed specifically for this study, was used to guide the discussion. The meetings were recorded, transcribed, and analysed thematically. Results: The scoping literature review revealed potential benefits but also barriers to the development of an integrated lifestyle dataset, and highlighted the need to consider local factors which need to be better understood prior to implementation. These findings from the literature were supported by rustults from the subsequent consultation exercise. Stakeholders for the most part, welcomed the idea of an integrated East Midlands lifestyle database because of its potential benefits for research and to produce evidence to inform service development and commissioning decisions. However, concerns were expressed by some providers including anxieties around revealing their business strategies to rival organisations also involved in the provision of lifestyles services, the cost of setting up and running the proposed integrated database, and the complexities involved in information sharing and governance arrangements which would need to be established. Conclusion: In view of the findings the following options should be explored while taking into consideration the barriers and facilitators expressed by stakeholders: 1. A fully integrated individual level lifestyle dataset across the whole East Midlands covering all four lifestyle areas, with governance and access controlled by one institution (possibly a Local Authority or a university) that will house and maintain the database. 2. A fully integrated individual level dataset for all four lifestyle areas, within just one geographical area to start with, which is owned by the service provider. There is a need to consider how to make this available more widely, as the providers only report collated data back to the commissioners. 3. A fully integrated individual level dataset initially starting with one health area (possibly smoking which already has a standardised Key Performance Indicators (KPI) across the whole region, (to be rolled out later subject to success), with governance and access controlled by the institution (either a Local Authority or a local university) that will house the database. 4. An integrated aggregated level dataset covering all four lifestyle areas (reporting similar KPIs as is done currently by service providers who report back to their commissioners), across the whole East Midlands, with governance and access controlled by one institution (possibly a Local Authority or a university) that will house and maintain the database. 5. A fully integrated aggregated level dataset for all four lifestyle areas, within just one geographical area to start with, as we have in Lincolnshire, which is owned by the service provider. There is a need to consider how to make this more widely available, as the providers only report collated data back to the commissioners. This is the model already used in Lincolnshire. 6. An integrated aggregated level dataset initially starting with one health area (possibly smoking which already has a standardised KPI) across the whole region, (to be rolled out later subject to success), with governance and access controlled by the institution (either a Local Authority or a local university) that will house the database

Measuring the impact of COVID-19 on hospital care pathways

Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

Research Paper: Process Mining and Synthetic Health Data: Reflections and Lessons Learnt

Analysing the treatment pathways in real-world health data can provide valuable insight for clinicians and decision-makers. However, the procedures for acquiring real-world data for research can be restrictive, time-consuming and risks disclosing identifiable information. Synthetic data might enable representative analysis without direct access to sensitive data. In the first part of our paper, we propose an approach for grading synthetic data for process analysis based on its fidelity to relationships found in real-world data. In the second part, we apply our grading approach by assessing cancer patient pathways in a synthetic healthcare dataset (The Simulacrum provided by the English National Cancer Registration and Analysis Service) using process mining. Visualisations of the patient pathways within the synthetic data appear plausible, showing relationships between events confirmed in the underlying non-synthetic data. Data quality issues are also present within the synthetic data which reflect real-world problems and artefacts from the synthetic dataset’s creation. Process mining of synthetic data in healthcare is an emerging field with novel challenges. We conclude that researchers should be aware of the risks when extrapolating results produced from research on synthetic data to real-world scenarios and assess findings with analysts who are able to view the underlying data

Validation of design artefacts for blockchain-enabled precision healthcare as a service.

Healthcare systems around the globe are currently experiencing a rapid wave of digital disruption. Current research in applying emerging technologies such as Big Data (BD), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Augmented Reality (AR), Virtual Reality (VR), Digital Twin (DT), Wearable Sensor (WS), Blockchain (BC) and Smart Contracts (SC) in contact tracing, tracking, drug discovery, care support and delivery, vaccine distribution, management, and delivery. These disruptive innovations have made it feasible for the healthcare industry to provide personalised digital health solutions and services to the people and ensure sustainability in healthcare. Precision Healthcare (PHC) is a new inclusion in digital healthcare that can support personalised needs. It focuses on supporting and providing precise healthcare delivery. Despite such potential, recent studies show that PHC is ineffectual due to the lower patient adoption in the system. Anecdotal evidence shows that people are refraining from adopting PHC due to distrust. This thesis presents a BC-enabled PHC ecosystem that addresses ongoing issues and challenges regarding low opt-in. The designed ecosystem also incorporates emerging information technologies that are potential to address the need for user-centricity, data privacy and security, accountability, transparency, interoperability, and scalability for a sustainable PHC ecosystem. The research adopts Soft System Methodology (SSM) to construct and validate the design artefact and sub-artefacts of the proposed PHC ecosystem that addresses the low opt-in problem. Following a comprehensive view of the scholarly literature, which resulted in a draft set of design principles and rules, eighteen design refinement interviews were conducted to develop the artefact and sub-artefacts for design specifications. The artefact and sub-artefacts were validated through a design validation workshop, where the designed ecosystem was presented to a Delphi panel of twenty-two health industry actors. The key research finding was that there is a need for data-driven, secure, transparent, scalable, individualised healthcare services to achieve sustainability in healthcare. It includes explainable AI, data standards for biosensor devices, affordable BC solutions for storage, privacy and security policy, interoperability, and usercentricity, which prompts further research and industry application. The proposed ecosystem is potentially effective in growing trust, influencing patients in active engagement with real-world implementation, and contributing to sustainability in healthcare