1,204 research outputs found
A Model for Using Physiological Conditions for Proactive Tourist Recommendations
Mobile proactive tourist recommender systems can support tourists by
recommending the best choice depending on different contexts related to herself
and the environment. In this paper, we propose to utilize wearable sensors to
gather health information about a tourist and use them for recommending tourist
activities. We discuss a range of wearable devices, sensors to infer
physiological conditions of the users, and exemplify the feasibility using a
popular self-quantification mobile app. Our main contribution then comprises a
data model to derive relations between the parameters measured by the wearable
sensors, such as heart rate, body temperature, blood pressure, and use them to
infer the physiological condition of a user. This model can then be used to
derive classes of tourist activities that determine which items should be
recommended
How will the Internet of Things enable Augmented Personalized Health?
Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that continuously collect health-related data from the environment and
inside the living quarters. Together, these have created an opportunity for a
new generation of healthcare solutions. However, interpreting data to
understand an individual's health is challenging. It is usually necessary to
look at that individual's clinical record and behavioral information, as well
as social and environmental information affecting that individual. Interpreting
how well a patient is doing also requires looking at his adherence to
respective health objectives, application of relevant clinical knowledge and
the desired outcomes.
We resort to the vision of Augmented Personalized Healthcare (APH) to exploit
the extensive variety of relevant data and medical knowledge using Artificial
Intelligence (AI) techniques to extend and enhance human health to presents
various stages of augmented health management strategies: self-monitoring,
self-appraisal, self-management, intervention, and disease progress tracking
and prediction. kHealth technology, a specific incarnation of APH, and its
application to Asthma and other diseases are used to provide illustrations and
discuss alternatives for technology-assisted health management. Several
prominent efforts involving IoT and patient-generated health data (PGHD) with
respect converting multimodal data into actionable information (big data to
smart data) are also identified. Roles of three components in an evidence-based
semantic perception approach- Contextualization, Abstraction, and
Personalization are discussed
General Conceptual Framework of Future Wearables in Healthcare: Unified, Unique, Ubiquitous, and Unobtrusive (U4) for Customized Quantified Output
We concentrate on the importance and future conceptual development of wearable devices as the major means of personalized healthcare. We discuss and address the role of wearables in the new era of healthcare in proactive medicine. This work addresses the behavioral, environmental, physiological, and psychological parameters as the most effective domains in personalized healthcare, and the wearables are categorized according to the range of measurements. The importance of multi-parameter, multi-domain monitoring and the respective interactions are further discussed and the generation of wearables based on the number of monitoring area(s) is consequently formulated
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
Exploring IoT in Smart Cities: Practices, Challenges and Way Forward
The rise of Internet of things (IoT) technology has revolutionized urban
living, offering immense potential for smart cities in which smart home, smart
infrastructure, and smart industry are essential aspects that contribute to the
development of intelligent urban ecosystems. The integration of smart home
technology raises concerns regarding data privacy and security, while smart
infrastructure implementation demands robust networking and interoperability
solutions. Simultaneously, deploying IoT in industrial settings faces
challenges related to scalability, standardization, and data management. This
research paper offers a systematic literature review of published research in
the field of IoT in smart cities including 55 relevant primary studies that
have been published in reputable journals and conferences. This extensive
literature review explores and evaluates various aspects of smart home, smart
infrastructure, and smart industry and the challenges like security and
privacy, smart sensors, interoperability and standardization. We provide a
unified perspective, as we seek to enhance the efficiency and effectiveness of
smart cities while overcoming security concerns. It then explores their
potential for collective integration and impact on the development of smart
cities. Furthermore, this study addresses the challenges associated with each
component individually and explores their combined impact on enhancing urban
efficiency and sustainability. Through a comprehensive analysis of security
concerns, this research successfully integrates these IoT components in a
unified approach, presenting a holistic framework for building smart cities of
the future. Integrating smart home, smart infrastructure, and smart industry,
this research highlights the significance of an integrated approach in
developing smart cities
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Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
Pragmatic Evaluation of Health Monitoring & Analysis Models from an Empirical Perspective
Implementing and deploying several linked modules that can conduct real-time analysis and recommendation of patient datasets is necessary for designing health monitoring and analysis models. These databases include, but are not limited to, blood test results, computer tomography (CT) scans, MRI scans, PET scans, and other imaging tests. A combination of signal processing and image processing methods are used to process them. These methods include data collection, pre-processing, feature extraction and selection, classification, and context-specific post-processing. Researchers have put forward a variety of machine learning (ML) and deep learning (DL) techniques to carry out these tasks, which help with the high-accuracy categorization of these datasets. However, the internal operational features and the quantitative and qualitative performance indicators of each of these models differ. These models also demonstrate various functional subtleties, contextual benefits, application-specific constraints, and deployment-specific future research directions. It is difficult for researchers to pinpoint models that perform well for their application-specific use cases because of the vast range of performance. In order to reduce this uncertainty, this paper discusses a review of several Health Monitoring & Analysis Models in terms of their internal operational features & performance measurements. Readers will be able to recognise models that are appropriate for their application-specific use cases based on this discussion. When compared to other models, it was shown that Convolutional Neural Networks (CNNs), Masked Region CNN (MRCNN), Recurrent NN (RNN), Q-Learning, and Reinforcement learning models had greater analytical performance. They are hence suitable for clinical use cases. These models' worse scaling performance is a result of their increased complexity and higher implementation costs. This paper compares evaluated models in terms of accuracy, computational latency, deployment complexity, scalability, and deployment cost metrics to analyse such scenarios. This comparison will help users choose the best models for their performance-specific use cases. In this article, a new Health Monitoring Metric (HMM), which integrates many performance indicators to identify the best-performing models under various real-time patient settings, is reviewed to make the process of model selection even easier for real-time scenarios
COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalised medicine
A comprehensive bibliographic review with R statistical methods of the COVID
pandemic in PubMed literature and Web of Science Core Collection, supported
with Google Scholar search. In addition, a case study review of emerging new
approaches in different regions, using medical literature, academic literature,
news articles and other reliable data sources. Public responses of mistrust
about privacy data misuse differ across countries, depending on the chosen
public communication strategy
THE INTERNET OF THINGS (IOT) IN DISASTER RESPONSE
Disaster management is a complex practice that relies on access to and the usability of critical information to develop strategies for effective decision-making. The emergence of wearable internet of things (IoT) technology has attracted the interests of several major industries, making it one of the fastest-growing technologies to date. This thesis asks, How can disaster management incorporate wearable IoT technology in operations and decision-making practices in disaster response? How IoT is applied in other prominent industries, including construction, manufacturing and distribution, the Department of Defense, and public safety, provides a basis for furthering its application to challenges affecting agency coordination. The critical needs of disaster intelligence in the context of hurricanes, structural collapses, and wildfires are scrutinized to identify gaps that wearable technology could address in terms of information-sharing in multi-agency coordination and the decision-making practices that routinely occur in disaster response. Last, the specifics of wearable technology from the perspective of the private consumer and commercial industry illustrate its potential to improve disaster response but also acknowledge certain limitations including technical capabilities and information privacy and security.Civilian, Virginia Beach Fire Department / FEMA - USAR VATF-2Approved for public release. Distribution is unlimited
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