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.

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. Here, we report a bio-compatible, flexible, noninvasive, low-cost piezoelectric sensor for continuous and real-time cardiovascular monitoring. The sensor, utilizing a thin aluminum nitride film on a flexible Kapton substrate, is used to extract heart rate, blood pressure waves, pulse wave velocities, and cardio-ankle vascular index from four arterial pulse sites: carotid, brachial, radial, and posterior tibial arteries. This simultaneous recording, for the first time in the same experiment, allows to provide a comprehensive cardiovascular patient's health profile. In a test with a 28-year-old male subject, the sensor yielded the SI = 7.1 ± 0.2 m/s, RI = 54.4 ± 0.5 %, MAP = 86.2 ± 1.5 mmHg, CAVI = 7.8 ± 0.2, and seven PWVs from the combination of the four different arterial positions, in good agreement with the typical values reported in the literature. These findings make the proposed technology a powerful tool to facilitate personalized medical diagnosis in preventing CVDs.