Architectural approach to the multisource health monitoring application design

Forward-looking personalized health care services tend to utilize benefits of smart spaces. Particularly, a smartphone app can be used as a hub for collecting and preprocessing of the vital health parameters provided by various sensors. Thus, the IoT-enabled mHealth apps should be designed to be easy extendable for the new kinds of data sources and processing units. In this paper, the architecture of mobile app supporting several data sources is described. Also, implementation issues related to Android and Windows Phone platforms are discussed

SMART-M3 v.0.9: A semantic event processing engine supporting information level interoperability in ambient intelligence

This tutorial is addressed to all students in Electronic Engineering and Information Engineering at the Scuola di Ingegneria e Architettura of the University of Bologna attending the following courses: Laboratory of Interoperability of Embedded Systems, "Calcolatori Elettronici M" and "Attività Progettuale di Calcolatori Elettronici M". This tutorial includes the guidelines to build distributed applications where clients may interact with physical space. Inter-client interaction occurs through a semantic event processing engine. Information interoperability is based on a shared knowledge representation model named ontology. This tutorial is focused on client design and on the SPARQL primitives that provide the means for client-event processing engine interaction

Medicine tracker for Smart TV

In this paper we estimate relevance of Smart TV as a platform for mobile healthcare applications. Suitability estimation is based on considering the roles that Smart TV can play in mobile healthcare area and benefits it provides. On top of our analysis we propose a prototype of a mobile healthcare application. Then, we present the actual application that is being developed accordingly to these specifications. Finally, we make a conclusion about relevance of Smart TV as a platform for mHealth applications

From Heterogeneous Sensor Networks to Integrated Software Services: Design and Implementation of a Semantic Architecture for the Internet of Things at ARCES@UNIBO

The Internet of Things (IoTs) is growing fast both in terms of number of devices connected and of complexity of deployments and applications. Several research studies an- alyzing the economical impact of the IoT worldwide identify the interoperability as one of the main boosting factor for its growth, thanks to the possibility to unlock novel commercial opportunities derived from the integration of heterogeneous systems which are currently not interconnected. However, at present, interoperability constitutes a relevant practical issue on any IoT deployments that is composed of sensor platforms mapped on different wireless technologies, network protocols or data formats. The paper addresses such issue, and investigates how to achieve effective data interoperability and data reuse on complex IoT deployments, where multiple users/applications need to consume sensor data produced by heterogeneous sensor networks. We propose a generic three-tier IoT architecture, which decouples the sensor data producers from the sensor data consumers, thanks to the intermediation of a semantic broker which is in charge of translating the sensor data into a shared ontology, and of providing publish-subscribe facilities to the producers/consumers. Then, we describe the real-world implementation of such architecture devised at the Advanced Research Center on Electronic System (ARCES) of the University of Bologna. The actual system collects the data produced by three different sensor networks, integrates them through a SPARQL Event Processing Architecture (SEPA), and supports two front- end applications for the data access, i.e. a web dashboard and an Amazon Alexa voice service

Improved algorithm for heart rate measurement using mobile phone camera

Nowadays a lot of different ways to measure person's heart rate are exist. One of such ways is using mobile phone. It is very easy for the person and do not require any special skills or buying special devices. All that is needed for heart rate measurement is mobile phone with on-board camera with flash equipped. In this paper we overview existing algorithms for heart rate measuring using mobile phone and propose improved algorithm, that is more efficient, than reviewed ones

Разработка алгоритма измерения частоты пульса человека с помощью камеры мобильного телефона

Nowadays there exist many different ways to measure a person’s heart rate. One of them assumes the usage of a mobile phone built-in camera. This method is easy to use and does not require any additional skills or special devices for heart rate measurement. It requires only a mobile cellphone with a built-in camera and a flash. The main idea of the method is to detect changes in finger skin color that occur due to blood pulsation. The measurement process is simple: the user covers the camera lens with a finger and the application on the mobile phone starts catching and analyzing frames from the camera. Heart rate can be calculated by analyzing average red component values of frames taken by the mobile cellphone camera that contain images of an area of the skin.In this paper the authors review the existing algorithms for heart rate measurement with the help of a mobile phone camera and propose their own algorithm which is more efficient than the reviewed algorithms.В настоящее время существует большое количество различных способов измерения частоты пульса человека. Один из таких способов связан с использованием камеры мобильного телефона. Он удобен и прост с точки зрения пользователя и не требует дополнительных знаний или покупки специальных устройств для измерения пульса. Все, что необходимо, — это мобильный телефон со встроенной камерой и вспышкой. Основная идея данного способа заключается в том, чтобы детектировать изменения цвета кожи пальца руки, которые возникают из-за пульсации крови. Процесс измерения выглядит очень просто: пользователь прикладывает палец к камере, после чего приложение на мобильном телефоне начинает захватывать и анализировать кадры, полученные с камеры. Анализируя средние значения красной компоненты кадров с камеры мобильного телефона, содержащих изображение участка кожи, можно сделать вывод о частоте пульса.В данной статье авторы делают обзор существующих алгоритмов для измерения частоты пульса с помощью мобильного телефона и предлагают собственный алгоритм, который более эффективен, чем рассмотренные.

Multi-Factor Authentication: A Survey

Today, digitalization decisively penetrates all the sides of the modern society. One of the key enablers to maintain this process secure is authentication. It covers many different areas of a hyper-connected world, including online payments, communications, access right management, etc. This work sheds light on the evolution of authentication systems towards Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). Particularly, MFA is expected to be utilized for human-to-everything interactions by enabling fast, user-friendly, and reliable authentication when accessing a service. This paper surveys the already available and emerging sensors (factor providers) that allow for authenticating a user with the system directly or by involving the cloud. The corresponding challenges from the user as well as the service provider perspective are also reviewed. The MFA system based on reversed Lagrange polynomial within Shamir’s Secret Sharing (SSS) scheme is further proposed to enable more flexible authentication. This solution covers the cases of authenticating the user even if some of the factors are mismatched or absent. Our framework allows for qualifying the missing factors by authenticating the user without disclosing sensitive biometric data to the verification entity. Finally, a vision of the future trends in MFA is discussed.Peer reviewe

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence

Are Machine Learning Methods the Future for Smoking Cessation Apps?

Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention