Enhancing cardiovascular health monitoring: Simultaneous multi-artery cardiac markers recording with flexible and bio-compatible AlN piezoelectric sensors.

Continuous monitoring of cardiovascular parameters like pulse wave velocity (PWV), blood pressure wave (BPW), stiffness index (SI), reflection index (RI), mean arterial pressure (MAP), and cardio-ankle vascular index (CAVI) has significant clinical importance for the early diagnosis of cardiovascular diseases (CVDs). Standard approaches, including echocardiography, impedance cardiography, or hemodynamic monitoring, are hindered by expensive and bulky apparatus and accessibility only in specialized facilities. Moreover, noninvasive techniques like sphygmomanometry, electrocardiography, and arterial tonometry often lack accuracy due to external electrical interferences, artifacts produced by unreliable electrode contacts, misreading from placement errors, or failure in detecting transient issues and trends.

Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices.

The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) to perform binary ("Normal"/"Pathologic") and multiclass ("Normal", "CAD" (coronary artery disease), "MVP" (mitral valve prolapse), and "Benign" (benign murmurs)) classification of PCG signals, without heart sound segmentation algorithms. Two datasets of 482 and 826 PCG signals from the Physionet/CinC 2016 dataset are used to train the binary and multiclass classifiers, respectively.

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.

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.

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.

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.

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.

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.

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.

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.

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.