A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards

[EN] The Internet of Things paradigm in healthcare has boosted the design of new solutions for the promotion of healthy lifestyles and the remote care. Thanks to the effort of academia and industry, there is a wide variety of platforms, systems and commercial products enabling the real-time information exchange of environmental data and people's health status. However, one of the problems of these type of prototypes and solutions is the lack of interoperability and the compromised scalability in large scenarios, which limits its potential to be deployed in real cases of application. In this paper, we propose a health monitoring system based on the integration of rapid prototyping hardware and interoperable software to build system capable of transmitting biomedical data to healthcare professionals. The proposed system involves Internet of Things technologies and interoperablility standards for health information exchange such as the Fast Healthcare Interoperability Resources and a reference framework architecture for Ambient Assisted Living UniversAAL.This research received no external funding. The APC was funded by Research group Information and Communication Technologies against Climate Change (!CTCC) of the Universitat Politecnica de Valencia, Spain.Lemus Zúñiga, LG.; Félix, JM.; Fides Valero, Á.; Benlloch-Dualde, J.; Martinez-Millana, A. (2022). A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards. Sensors. 22(4):1-17. https://doi.org/10.3390/s2204164611722

SFTSDH: Applying Spring Security Framework with TSD-Based OAuth2 to Protect Microservice Architecture APIs

The Internet of Medical Things (IoMT) combines medical devices and applications that use network technologies to connect healthcare information systems (HIS). IoMT is reforming the medical industry by adopting information and communication technologies (ICTs). Identity verification, secure collection, and exchange of medical data are essential in health applications. In this study, we implemented a hybrid security solution to secure the collection and management of personal health data using Spring Framework (SF), Services for Sensitive Data (TSD) as a service platform, and Hyper-Text-Transfer-Protocol (HTTP (H)) security methods. The adopted solution (SFTSDH = SF + TSD + H) instigated the following security features: identity brokering, OAuth2, multifactor authentication, and access control to protect the Microservices Architecture Application Programming Interfaces (APIs), following the General Data Protection Regulation (GDPR). Moreover, we extended the adopted security solution to develop a digital infrastructure to facilitate the research and innovation work in the electronic health (eHealth) section, focusing on solution validation with theoretical evaluation and experimental testing. We used a web engineering security methodology to achieve and explain the adopted security solution. As a case study, we designed and implemented electronic coaching (eCoaching) prototype system and deployed the same in the developed infrastructure to securely record and share personal health data. Furthermore, we compared the test results with related studies qualitatively for the efficient evaluation of the implemented security solution. The SFTSDH implementation and configuration in the prototype system have effectively secured the eCoach APIs from an attack in all the considered scenarios. The eCoach prototype with the SFTSDH solution effectively sustained a load of (≈) 1000 concurrent users in the developed digital health infrastructure. In addition, we performed a qualitative comparison among the following security solutions: SF security, third-party security, and SFTSDH, where SFTSDH showed a promising outcome.publishedVersio

The MASSIF platform : a modular and semantic platform for the development of flexible IoT services

In the Internet of Things (IoT), data-producing entities sense their environment and transmit these observations to a data processing platform for further analysis. Applications can have a notion of context awareness by combining this sensed data, or by processing the combined data. The processes of combining data can consist both of merging the dynamic sensed data, as well as fusing the sensed data with background and historical data. Semantics can aid in this task, as they have proven their use in data integration, knowledge exchange and reasoning. Semantic services performing reasoning on the integrated sensed data, combined with background knowledge, such as profile data, allow extracting useful information and support intelligent decision making. However, advanced reasoning on the combination of this sensed data and background knowledge is still hard to achieve. Furthermore, the collaboration between semantic services allows to reach complex decisions. The dynamic composition of such collaborative workflows that can adapt to the current context, has not received much attention yet. In this paper, we present MASSIF, a data-driven platform for the semantic annotation of and reasoning on IoT data. It allows the integration of multiple modular reasoning services that can collaborate in a flexible manner to facilitate complex decision-making processes. Data-driven workflows are enabled by letting services specify the data they would like to consume. After thorough processing, these services can decide to share their decisions with other consumers. By defining the data these services would like to consume, they can operate on a subset of data, improving reasoning efficiency. Furthermore, each of these services can integrate the consumed data with background knowledge in its own context model, for rapid intelligent decision making. To show the strengths of the platform, two use cases are detailed and thoroughly evaluated

A human-centered Web-based tool for the effective real-time motion data collection and annotation from BLE IoT devices

The effective utilization of real-world data is an integral part of any IoT monitoring or AI-assisted system. Thus, data collection and annotation is an important step towards the successful development and realization of such systems. Nevertheless, in order to create reliable datasets, current data collection and annotation methodologies often require a controlled environment while also the presence of the volunteer contributing to the process, or any subject for that matter, and an expert, monitoring the procedure, is mandatory. These processes are heavily restrained by the recent COVID-19 pandemic outbreak.To address such issues, in this paper we propose a human-centered Web-based dataset creation and annotation tool that utilizes the Web Bluetooth API. The user can effectively collect gestures from a nearby device that supports the BLE protocol, assign tags to the collected data, and store them remotely, in real-time. The data storage, as well as its annotation, can also be performed remotely by an expert stakeholder. An off-the-shelf wearable sensorial device has been used indicatively for our tool demonstration purposes. To the best of our knowledge, this is the first attempt that exploits the Web Bluetooth API capabilities for the development of a Browser-based real-time data collection, storage, and annotation tool. Our tool can be also expanded to other applications that use the sensing device with only minor configuration changes and is also operable through any smart-device that supports a Web-Browser. Furthermore, our tool's performance matches that of native applications'. Finally, the tool is successfully deployed and validated by integrating it into our ongoing ML platform that is related to allergic rhinitis gesture recognition

Predictive Analytics in Healthcare: Empowering Consultation with Machine Learning

The Smart Healthcare and Online Consultation initiative intends to offer patients a quick and convenient online platform for seeking medical advice and services. Real-time video consultations, appointment scheduling, prescription administration, and health records management are just a few of the capabilities available on the platform. To deliver individualized and superior healthcare services, the initiative to use cutting-edge such as AI, ML, and data analytics. By giving patients an easy and affordable way to receive healthcare services remotely, the Smart Healthcare and Online Consultation initiative has the potential to completely transform the healthcare sector

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

The Proceedings of 14th Australian Information Security Management Conference, 5-6 December 2016, Edith Cowan University, Perth, Australia

The annual Security Congress, run by the Security Research Institute at Edith Cowan University, includes the Australian Information Security and Management Conference. Now in its fourteenth year, the conference remains popular for its diverse content and mixture of technical research and discussion papers. The area of information security and management continues to be varied, as is reflected by the wide variety of subject matter covered by the papers this year. The conference has drawn interest and papers from within Australia and internationally. All submitted papers were subject to a double blind peer review process. Fifteen papers were submitted from Australia and overseas, of which ten were accepted for final presentation and publication. We wish to thank the reviewers for kindly volunteering their time and expertise in support of this event. We would also like to thank the conference committee who have organised yet another successful congress. Events such as this are impossible without the tireless efforts of such people in reviewing and editing the conference papers, and assisting with the planning, organisation and execution of the conferences. To our sponsors also a vote of thanks for both the financial and moral support provided to the conference. Finally, thank you to the administrative and technical staff, and students of the ECU Security Research Institute for their contributions to the running of the conference

Internet of Things Based Technology for Smart Home System: A Generic Framework

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