Human Coaching Methodologies for Automatic Electronic Coaching (eCoaching) as Behavioral Interventions with Information and Communication Technology: Systematic Review

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Automatic Generation of Personalized Recommendations in eCoaching

Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors

Diabetes Management System for a New Type 2 Diabetes Geriatric Cohort: Improve the Interaction of Self-management

abstract: According to the ADA (American Diabetes Association), diabetes mellitus is one of the chronic diseases with the highest mortality rate. In the US, 25 million are known diabetics, which may double in the next decade, and another seven million are undiagnosed. Among these patients, older adults are a very special group with varying physical capabilities, cognitive functions and life expectancies. Because they run an increased risk for geriatric conditions, Type 2 diabetes treatments for them must be both realistic and systematic. In fact, some researchers have explored older adults’ experiences of diabetes, and how they manage their diabetes with new technological devices. However, little research has focused on their emotional experiences of medical treatment technology, such as mobile applications, tablets, and websites for geriatric diabetes. This study will address both elderly people's experiences and reactions to devices and their children's awareness of diabetes. It aims to find out how to improve the diabetes treatment and create a systematic diabetes mobile application that combines self-initiated and assisted care together.Dissertation/ThesisMasters Thesis Design 201

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

Use of Technology and Big Data in E-Health Services

[Abstract]: The objective of this work has been to analyze the factors that determine the acceptance and use of technology (TAM) in the field of health services as well as the design of an app focused on the prevention of cardiovascular diseases. The factors that determine the use of electronic devices in the health field are the utility or perceived value, the ease of use (simple and attractive interface), the interactivity of the user with the device, the attitude towards technology and the reduction of the perceived risk (protection of privacy and health risk). From these determining factors, an app named Heart Focus App has been developed. This app would also make it possible to collect massive data from users or from databases from different official sources (FAO, INE, Ministry of Health) with the aim of predicting risk factors and providing information on healthier lifestyle habits. A data analysis based on statistical analysis techniques such as correlation analysis has identified a strong association between the elderly population and deaths from cardiovascular disease. Therefore, in view of the inevitable aging of the population, the development and use of electronic devices or apps with simple and easy-to-use interfaces, and the exploitation of big data derived from these can allow not only to improve the quality of life of patients, but also to reduce health costs and improve the quality of online and offline service.[Resumen]: El objetivo de este trabajo ha sido analizar los factores que determinan la aceptación y uso de la tecnología (TAM) en el ámbito de los servicios sanitarios así como el diseño de una app focalizada en la prevención de enfermedades cardiovasculares. Los factores que determinan el uso de dispositivos electrónicos en el ámbito sanitario son la utilidad o valor percibido, la facilidad de uso (interfaz sencilla y atractiva), la interactividad del usuario con el dispositivo, la actitud hacia la tecnología y la reducción del riesgo percibido (protección de la privacidad y los riesgos para la salud). A partir de estos factores determinantes, se ha desarrollado una app denominada Heart Focus App. Esta app también permitiría recoger datos masivos procedentes de los usuarios o de bancos de datos de diferentes fuentes oficiales (FAO, INE, Ministerio de Sanidad) con el objetivo de predecir factores de riesgo y proporcionar información sobre hábitos de vida más saludables. Un análisis de datos basado en técnicas de análisis estadístico como el análisis de correlación ha identificado una fuerte asociación entre la población de edad avanzada y las muertes por enfermedades cardiovasculares. Por ello, ante el inevitable envejecimiento de la población, el desarrollo y uso de dispositivos electrónicos o apps con interfaces sencillas y fáciles de usar, y la explotación de datos masivos derivados de estas puede permitir no solo mejorar la calidad de vida de los pacientes, sino también disminuir los costes sanitarios y mejorar la calidad del servicio online y offline.Traballo fin de grao (UDC.ECO). ADE. Curso 2019/202

Health State Estimation

Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin