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.

Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications.

Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture.

An IoT-Aware Solution to Support Governments in Air Pollution Monitoring Based on the Combination of Real-Time Data and Citizen Feedback.

One of the main concerns of the last century is regarding the air pollution and its effects caused on human health. Its impact is particularly evident in cities and urban areas where governments are trying to mitigate its effects. Although different solutions have been already proposed, citizens continue to report bad conditions in the areas in which they live. This paper proposes a solution to support governments in monitoring the city pollution through the combination of user feedbacks/reports and real-time data acquired through dedicated mobile IoT sensors dynamically re-located by government officials to verify the reported conditions of specific areas. The mobile devices leverage on dedicated sensors to monitor the air quality and capture main roads traffic conditions through machine learning techniques. The system exposes a mobile application and a website to support the collection of citizens' reports and show gathered data to both institutions and end-users. A proof-of-concept of the proposed solution has been prototyped in a medium-sized university campus. Both the performance and functional validation have demonstrated the feasibility and the effectiveness of the system and allowed the definition of some lessons learned, as well as future works.

Behavior Modeling for a Beacon-Based Indoor Location System.

In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system's performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user's next location with 67% accuracy.

A Smart and Secure Logistics System Based on IoT and Cloud Technologies.

Recently, one of the hottest topics in the logistics sector has been the traceability of goods and the monitoring of their condition during transportation. Perishable goods, such as fresh goods, have specifically attracted attention of the researchers that have already proposed different solutions to guarantee quality and freshness of food through the whole cold chain. In this regard, the use of Internet of Things (IoT)-enabling technologies and its specific branch called edge computing is bringing different enhancements thereby achieving easy remote and real-time monitoring of transported goods. Due to the fast changes of the requirements and the difficulties that researchers can encounter in proposing new solutions, the fast prototype approach could contribute to rapidly enhance both the research and the commercial sector. In order to make easy the fast prototyping of solutions, different platforms and tools have been proposed in the last years, however it is difficult to guarantee end-to-end security at all the levels through such platforms. For this reason, based on the experiments reported in literature and aiming at providing support for fast-prototyping, end-to-end security in the logistics sector, the current work presents a solution that demonstrates how the advantages offered by the Azure Sphere platform, a dedicated hardware (i.e., microcontroller unit, the MT3620) device and Azure Sphere Security Service can be used to realize a fast prototype to trace fresh food conditions through its transportation. The proposed solution guarantees end-to-end security and can be exploited by future similar works also in other sectors.

Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future.

The rapid development and implementation of smart and IoT (Internet of Things) based technologies have allowed for various possibilities in technological advancements for different aspects of life. The main goal of IoT technologies is to simplify processes in different fields, to ensure a better efficiency of systems (technologies or specific processes) and finally to improve life quality. Sustainability has become a key issue for population where the dynamic development of IoT technologies is bringing different useful benefits, but this fast development must be carefully monitored and evaluated from an environmental point of view to limit the presence of harmful impacts and ensure the smart utilization of limited global resources. Significant research efforts are needed in the previous sense to carefully investigate the pros and cons of IoT technologies. This review editorial is partially directed on the research contributions presented at the 4th International Conference on Smart and Sustainable Technologies held in Split and Bol, Croatia, in 2019 (SpliTech 2019) as well as on recent findings from literature. The SpliTech2019 conference was a valuable event that successfully linked different engineering professions, industrial experts and finally researchers from academia. The focus of the conference was directed towards key conference tracks such as Smart City, Energy/Environment, e-Health and Engineering Modelling. The research presented and discussed at the SpliTech2019 conference helped to understand the complex and intertwined effects of IoT technologies on societies and their potential effects on sustainability in general. Various application areas of IoT technologies were discussed as well as the progress made. Four main topical areas were discussed in the herein editorial, i.e. latest advancements in the further fields: (i) IoT technologies in Sustainable Energy and Environment, (ii) IoT enabled Smart City, (iii) E-health - Ambient assisted living systems (iv) IoT technologies in Transportation and Low Carbon Products. The main outcomes of the review introductory article contributed to the better understanding of current technological progress in IoT application areas as well as the environmental implications linked with the increased application of IoT products.

An Innovative AAL System Based on IoT Technologies for Patients with Sarcopenia.

Sarcopenia is a highly prevalent, age-related muscle disorder associated with adverse outcomes. It is very important from a medical point of view to periodically monitor patients at risk of developing sarcopenia in order to early detect its onset or progression through objective and specific indicators. Today, the emerging Internet of Things (IoT)-enabling technologies allow us to create innovative, wearable, and non-invasive systems that can offer useful clinical support in this area. This work is focused on the use of combined hardware and software technologies, enabling the IoT, in order to monitor people suffering from sarcopenia by offering a high value-added service in the field of the Ambient Assisted Living (AAL). In addition to the description of the proposed system architecture, a validation of the entire system is also included, from both a performance and a functional point of view. Test beds have been carried out by using the independent replications method, and all measurements related to the identified sarcopenia parameters are characterized by a 95% confidence interval with a 5% maximum relative error. The implementation of these technologies as a supporting clinical tool used in a specific setting could significantly impact the life and independence of the sarcopenic frail elderly population.

An animal tracking system for behavior analysis using radio frequency identification.

Evaluating the behavior of mice and rats has substantially contributed to the progress of research in many scientific fields. Researchers commonly observe recorded video of animal behavior and manually record their observations for later analysis, but this approach has several limitations. The authors developed an automated system for tracking and analyzing the behavior of rodents that is based on radio frequency identification (RFID) in an ultra-high-frequency bandwidth. They provide an overview of the system's hardware and software components as well as describe their technique for surgically implanting passive RFID tags in mice. Finally, the authors present the findings of two validation studies to compare the accuracy of the RFID system versus commonly used approaches for evaluating the locomotor activity and object exploration of mice.

Evaluation of thermal and non-thermal effects of UHF RFID exposure on biological drugs.

The Radio Frequency Identification (RFID) technology promises to improve several processes in the healthcare scenario, especially those related to traceability of people and things. Unfortunately, there are still some barriers limiting the large-scale deployment of these innovative technologies in the healthcare field. Among these, the evaluation of potential thermal and non-thermal effects due to the exposure of biopharmaceutical products to electromagnetic fields is very challenging, but still slightly investigated. This paper aims to setup a controlled RF exposure environment, in order to reproduce a worst-case exposure of pharmaceutical products to the electromagnetic fields generated by the UHF RFID devices placed along the supply chain. Radiated powers several times higher than recommended by current normative limits were applied (10 W and 20 W). The electric field strength at the exposed sample location, used in tests, was as high as 100 V/m. Non-thermal effects were evaluated by chromatography techniques and in vitro assays. The results obtained for a particular case study, the ActrapidTM human insulin preparation, showed temperature increases lower than 0.5 °C and no significant changes in the structure and performance of the considered drug.

Enhanced UHF RFID tags for drug tracing.

Radio Frequency Identification (RFID) technology is playing a crucial role for item-level tracing systems in healthcare scenarios. The pharmaceutical supply chain is a fascinating application context, where RFID can guarantee transparency in the drug flow, supporting both suppliers and consumers against the growing counterfeiting problem. In such a context, the choice of the most adequate RFID tag, in terms of shape, frequency, size and reading range, is crucial. The potential presence of items containing materials hostile to the electromagnetic propagation exasperates the problem. In addition, the peculiarities of the different RFID-based checkpoints make even more stringent the requirements for the tag. In this work, the performance of several commercial UHF RFID tags in each step of the pharmaceutical supply chain has been evaluated, confirming the expected criticality. On such basis, a guideline for the electromagnetic design of new high-performance tags capable to overcome such criticalities has been defined. Finally, driven by such guidelines, a new enhanced tag has been designed, realized and tested. Due to patent pending issues, the antenna shape is not shown. Nevertheless, the optimal obtained results do not lose their validity. Indeed, on the one hand they demonstrate that high performance item level tracing systems can actually be implemented also in critical operating conditions. On the other hand, they encourage the tag designer to follow the identified guidelines so to realize enhanced UHF tags.