363 research outputs found
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A Secure Occupational Therapy Framework for Monitoring Cancer Patientsâ Quality of Life
Once diagnosed with cancer, a patient goes through a series of diagnosis and tests, which are referred to as âafter cancer treatmentâ. Due to the nature of the treatment and side effects, maintaining quality of life (QoL) in the home environment is a challenging task. Sometimes, a cancer patientâs situation changes abruptly as the functionality of certain organs deteriorates, which affects their QoL. One way of knowing the physiological functional status of a cancer patient is to design an occupational therapy. In this paper, we propose a blockchain and off-chain-based framework, which will allow multiple medical and ambient intelligent Internet of Things sensors to capture the QoL information from oneâs home environment and securely share it with their community of interest. Using our proposed framework, both transactional records and multimedia big data can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patientâs data available from an in-home secure monitoring system for a better understanding of a patientâs current or historical states. Finally, we will present our current implementation status, which provides significant encouragement for further development
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
Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey
Ubiquitous in-home health monitoring systems have become popular in recent
years due to the rise of digital health technologies and the growing demand for
remote health monitoring. These systems enable individuals to increase their
independence by allowing them to monitor their health from the home and by
allowing more control over their well-being. In this study, we perform a
comprehensive survey on this topic by reviewing a large number of literature in
the area. We investigate these systems from various aspects, namely sensing
technologies, communication technologies, intelligent and computing systems,
and application areas. Specifically, we provide an overview of in-home health
monitoring systems and identify their main components. We then present each
component and discuss its role within in-home health monitoring systems. In
addition, we provide an overview of the practical use of ubiquitous
technologies in the home for health monitoring. Finally, we identify the main
challenges and limitations based on the existing literature and provide eight
recommendations for potential future research directions toward the development
of in-home health monitoring systems. We conclude that despite extensive
research on various components needed for the development of effective in-home
health monitoring systems, the development of effective in-home health
monitoring systems still requires further investigation.Comment: 35 pages, 5 figure
Improving Access and Mental Health for Youth Through Virtual Models of Care
The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14â25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial
The Impact of Digital Technologies on Public Health in Developed and Developing Countries
This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic
Data Sovereignty in Data Donation Cycles - Requirements and Enabling Technologies for the Data-driven Development of Health Applications
Personalized healthcare is expected to increase the efficiency and the effectiveness of health services using different kinds of algorithms on existing data. This approach is currently confronted with the lack of digital data and the desire for self-determined personal data handling. However, the issue of health data donation is on the political agenda of some governments. Within this work, a knowledge base will be created by reviewing existing approaches and technologies regarding this topic with the focus on chronic diseases. A list of requirements will be derived from which we conceptualize a data donation cycle to demonstrate the challenges and opportunities of health data sovereignty and its future possibilities concerning data-driven health application development. By linking the requirements to technological approaches, the baseline for future open ecosystems will be presented
Human Digital Twin: A Survey
Digital twin has recently attracted growing attention, leading to intensive
research and applications. Along with this, a new research area, dubbed as
"human digital twin" (HDT), has emerged. Similar to the conception of digital
twin, HDT is referred to as the replica of a physical-world human in the
digital world. Nevertheless, HDT is much more complicated and delicate compared
to digital twins of any physical systems and processes, due to humans' dynamic
and evolutionary nature, including physical, behavioral, social, physiological,
psychological, cognitive, and biological dimensions. Studies on HDT are
limited, and the research is still in its infancy. In this paper, we first
examine the inception, development, and application of the digital twin
concept, providing a context within which we formally define and characterize
HDT based on the similarities and differences between digital twin and HDT.
Then we conduct an extensive literature review on HDT research, analyzing
underpinning technologies and establishing typical frameworks in which the core
HDT functions or components are organized. Built upon the findings from the
above work, we propose a generic architecture for the HDT system and describe
the core function blocks and corresponding technologies. Following this, we
present the state of the art of HDT technologies and applications in the
healthcare, industry, and daily life domain. Finally, we discuss various issues
related to the development of HDT and point out the trends and challenges of
future HDT research and development
Networking Architecture and Key Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey
Digital twin (DT), refers to a promising technique to digitally and
accurately represent actual physical entities. One typical advantage of DT is
that it can be used to not only virtually replicate a system's detailed
operations but also analyze the current condition, predict future behaviour,
and refine the control optimization. Although DT has been widely implemented in
various fields, such as smart manufacturing and transportation, its
conventional paradigm is limited to embody non-living entities, e.g., robots
and vehicles. When adopted in human-centric systems, a novel concept, called
human digital twin (HDT) has thus been proposed. Particularly, HDT allows in
silico representation of individual human body with the ability to dynamically
reflect molecular status, physiological status, emotional and psychological
status, as well as lifestyle evolutions. These prompt the expected application
of HDT in personalized healthcare (PH), which can facilitate remote monitoring,
diagnosis, prescription, surgery and rehabilitation. However, despite the large
potential, HDT faces substantial research challenges in different aspects, and
becomes an increasingly popular topic recently. In this survey, with a specific
focus on the networking architecture and key technologies for HDT in PH
applications, we first discuss the differences between HDT and conventional
DTs, followed by the universal framework and essential functions of HDT. We
then analyze its design requirements and challenges in PH applications. After
that, we provide an overview of the networking architecture of HDT, including
data acquisition layer, data communication layer, computation layer, data
management layer and data analysis and decision making layer. Besides reviewing
the key technologies for implementing such networking architecture in detail,
we conclude this survey by presenting future research directions of HDT
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