INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life

Human Action Recognition and Monitoring in Ambient Assisted Living Environments

Population ageing is set to become one of the most significant challenges of the 21st century, with implications for almost all sectors of society. Especially in developed countries, governments should immediately implement policies and solutions to facilitate the needs of an increasingly older population. Ambient Intelligence (AmI) and in particular the area of Ambient Assisted Living (AAL) offer a feasible response, allowing the creation of human-centric smart environments that are sensitive and responsive to the needs and behaviours of the user. In such a scenario, understand what a human being is doing, if and how he/she is interacting with specific objects, or whether abnormal situations are occurring is critical. This thesis is focused on two related research areas of AAL: the development of innovative vision-based techniques for human action recognition and the remote monitoring of users behaviour in smart environments. The former topic is addressed through different approaches based on data extracted from RGB-D sensors. A first algorithm exploiting skeleton joints orientations is proposed. This approach is extended through a multi-modal strategy that includes the RGB channel to define a number of temporal images, capable of describing the time evolution of actions. Finally, the concept of template co-updating concerning action recognition is introduced. Indeed, exploiting different data categories (e.g., skeleton and RGB information) improve the effectiveness of template updating through co-updating techniques. The action recognition algorithms have been evaluated on CAD-60 and CAD-120, achieving results comparable with the state-of-the-art. Moreover, due to the lack of datasets including skeleton joints orientations, a new benchmark named Office Activity Dataset has been internally acquired and released. Regarding the second topic addressed, the goal is to provide a detailed implementation strategy concerning a generic Internet of Things monitoring platform that could be used for checking users' behaviour in AmI/AAL contexts

WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City

The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by retrieving device and app usage patterns. However, a still uncovered research topic is to extend the concepts of behavioral and context-aware biometric to take into account all the sensing data provided by the Internet of Things (IoT) and the smart city, in the shape of user habits. In this paper, we propose a meta-model-driven approach to mine user habits, by means of a combination of IoT data incoming from several sources such as smart mobility, smart metering, smart home, wearables and so on. Then, we use those habits to seamlessly authenticate users in real time all along the smart city when the same behavior occurs in different context and with different sensing technologies. Our model, which we called WoX+, allows the automatic extraction of user habits using a novel Artificial Intelligence (AI) technique focused on high-level concepts. The aim is to continuously authenticate the users using their habits as behavioral biometric, independently from the involved sensing hardware. To prove the effectiveness of WoX+ we organized a quantitative and qualitative evaluation in which 10 participants told us a spending habit they have involving the use of IoT. We chose the financial domain because it is ubiquitous, it is inherently multi-device, it is rich in time patterns, and most of all it requires a secure authentication. With the aim of extracting the requirement of such a system, we also asked the cohort how they expect WoX+ will use such habits to securely automatize payments and identify them in the smart city. We discovered that WoX+ satisfies most of the expected requirements, particularly in terms of unobtrusiveness of the solution, in contrast with the limitations observed in the existing studies. Finally, we used the responses given by the cohorts to generate synthetic data and train our novel AI block. Results show that the error in reconstructing the habits is acceptable: Mean Squared Error Percentage (MSEP) 0.04%

Programming frameworks for mobile sensing

The proliferation of smart mobile devices in people’s daily lives is making context-aware computing a reality. A plethora of sensors available in these devices can be utilized to understand users’ context better. Apps can provide more relevant data or services to the user based on improved understanding of user’s context. With the advent of cloud-assisted mobile platforms, apps can also perform collaborative computation over the sensing data collected from a group of users. However, there are still two main issues: (1) A lack of simple and effective personal sensing frameworks: existing frameworks do not provide support for real-time fusing of data from motion and visual sensors in a simple manner, and no existing framework collectively utilizes sensors from multiple personal devices and personal IoT sensors, and (2) a lack of collaborative/distributed computing frameworks for mobile users. This dissertation presents solutions for these two issues. The first issue is addressed by TagPix and Sentio, two frameworks for mobile sensing. The second issue is addressed by Moitree, a middleware for mobile distributed computing, and CASINO, a collaborative sensor-driven offloading system. TagPix is a real-time, privacy preserving photo tagging framework, which works locally on the phones and consumes little resources (e.g., battery). It generates relevant tags for landscape photos by utilizing sensors of a mobile device and it does not require any previous training or indexing. When a user aims the mobile camera to a particular landmark, the framework uses accelerometer and geomagnetic field sensor to identify in which direction the user is aiming the camera at. It then uses a landmark database and employs a smart distance estimation algorithm to identify which landmark(s) is targeted by the user. The framework then generates relevant tags for the captured photo using these information. A more versatile sensing framework can be developed using sensors from multiple devices possessed by a user. Sentio is such a framework which enables apps to seamlessly utilize the collective sensing capabilities of the user’s personal devices and of the IoT sensors located in the proximity of the user. With Sentio, an app running on any personal mobile/wearable device can access any sensor of the user in real-time using the same API, can selectively switch to the most suitable sensor of a particular type when multiple sensors of this type are available at different devices, and can build composite sensors. Sentio offers seamless connectivity to sensors even if the sensor-accessing code is offloaded to the cloud. Sentio provides these functionalities with a high-level API and a distributed middleware that handles all low-level communication and sensor management tasks. This dissertation also proposes Moitree, a middleware for the mobile cloud platforms where each mobile device is augmented by an avatar, a per-user always-on software entity that resides in the cloud. Mobile-avatar pairs participate in distributed computing as a unified computing entity. Moitree provides a common programming and execution framework for mobile distributed apps. Moitree allows the components of a distributed app to execute seamlessly over a set of mobile/avatar pairs, with the provision of offloading computation and communication to the cloud. The programming framework has two key features: user collaborations are modeled using group semantics - groups are created dynamically based on context and are hierarchical; data communication among group members is offloaded to the cloud through high-level communication channels. Finally, this dissertation presents and discusses CASINO, a collaborative sensor-driven computation offloading framework which can be used alongside Moitree. This framework includes a new scheduling algorithm which minimizes the total completion time of a collaborative computation that executes over a set of mobile/avatar pairs. Using the CASINO API, the programmers can mark their classes and functions as ”offloadable”. The framework collects profiling information (network, CPU, battery, etc.) from participating users’ mobile devices and avatars, and then schedules ”offloadable” tasks in mobiles and avatars in a way that reduces the total completion time. The scheduling problem is proven to be NP-Hard and there is no polynomial time optimization algorithm for it. The proposed algorithm can generate a schedule in polynomial time using a topological sorting and greedy technique

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

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 14–25 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

An IoT based Virtual Coaching System (VSC) for Assisting Activities of Daily Life

Nowadays aging of the population is becoming one of the main concerns of theworld. It is estimated that the number of people aged over 65 will increase from 461million to 2 billion in 2050. This substantial increment in the elderly population willhave significant consequences in the social and health care system. Therefore, in thecontext of Ambient Intelligence (AmI), the Ambient Assisted Living (AAL) has beenemerging as a new research area to address problems related to the aging of the population. AAL technologies based on embedded devices have demonstrated to be effectivein alleviating the social- and health-care issues related to the continuous growing of theaverage age of the population. Many smart applications, devices and systems have beendeveloped to monitor the health status of elderly, substitute them in the accomplishment of activities of the daily life (especially in presence of some impairment or disability),alert their caregivers in case of necessity and help them in recognizing risky situations.Such assistive technologies basically rely on the communication and interaction be-tween body sensors, smart environments and smart devices. However, in such contextless effort has been spent in designing smart solutions for empowering and supportingthe self-efficacy of people with neurodegenerative diseases and elderly in general. Thisthesis fills in the gap by presenting a low-cost, non intrusive, and ubiquitous VirtualCoaching System (VCS) to support people in the acquisition of new behaviors (e.g.,taking pills, drinking water, finding the right key, avoiding motor blocks) necessary tocope with needs derived from a change in their health status and a degradation of theircognitive capabilities as they age. VCS is based on the concept of extended mind intro-duced by Clark and Chalmers in 1998. They proposed the idea that objects within theenvironment function as a part of the mind. In my revisiting of the concept of extendedmind, the VCS is composed of a set of smart objects that exploit the Internet of Things(IoT) technology and machine learning-based algorithms, in order to identify the needsof the users and react accordingly. In particular, the system exploits smart tags to trans-form objects commonly used by people (e.g., pillbox, bottle of water, keys) into smartobjects, it monitors their usage according to their needs, and it incrementally guidesthem in the acquisition of new behaviors related to their needs. To implement VCS, thisthesis explores different research directions and challenges. First of all, it addresses thedefinition of a ubiquitous, non-invasive and low-cost indoor monitoring architecture byexploiting the IoT paradigm. Secondly, it deals with the necessity of developing solu-tions for implementing coaching actions and consequently monitoring human activitiesby analyzing the interaction between people and smart objects. Finally, it focuses on the design of low-cost localization systems for indoor environment, since knowing theposition of a person provides VCS with essential information to acquire information onperformed activities and to prevent risky situations. In the end, the outcomes of theseresearch directions have been integrated into a healthcare application scenario to imple-ment a wearable system that prevents freezing of gait in people affected by Parkinson\u2019sDisease

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

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe