Ilaria Sergi

Chronic Heart Failure (CHF) represents a significant global health concern due to its high morbidity and mortality rates. Effectively addressing this challenge requires scalable technology solutions to shift Heart Failure (HF) care from episodic reactive treatment to continuous personalized management. As digital health technologies advance, integrating Artificial Intelligence (AI) and the Internet of Things (IoT) into CHF care enables the development of scalable monitoring, prevention, and management strategies and real-time Clinical Decision Support Systems (CDSSs). This Systematic Literature Review (SLR) analyzes 67 peer-reviewed studies published between January 2021 and May 2024, selected using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to evaluate the technological and clinical impacts of AI-enabled systems in CHF and broader HF care. The review identifies emerging trends, discusses dataset characteristics and clinical relevance, identifies IoT integration patterns, gaps, and deployment barriers, and highlights opportunities for improving the integration of AI/IoT systems into HF care workflows. The studies are organized into four clinical application domains: HF detection, phenotyping and classification, risk stratification, and other miscellaneous applications. Our findings highlight the progress in AI/IoT synergy; however, challenges remain in dataset heterogeneity and coverage, reproducibility, benchmarking practices, and clinical workflow integration, particularly as IoT integration is often limited or insufficiently explored. Our primary recommendations emphasize the use of multimodal datasets, the adoption of interpretable modeling approaches, and stronger interdisciplinary collaboration to improve clinical applicability and support integration into real-world settings.

The aging global population requires innovative remote monitoring systems to assist doctors and caregivers in assessing the health of elderly patients. Doctors often lack access to continuous behavioral data, making it difficult to detect deviations from normal patterns when elderly patients arrive for a consultation. Without historical insights into common behaviors and potential anomalies detected with unobtrusive techniques (e.g., non-wearable devices), timely and informed medical interventions become challenging. To address this, we propose an edge-based Internet of Things (IoT) framework that enables real-time monitoring and anomaly detection using non-wearable sensors to assist doctors and caregivers in assessing the health of elderly patients. By processing data locally, the system minimizes privacy concerns and ensures immediate data availability, allowing healthcare professionals to detect unusual behavioral patterns early. The system employs advanced machine learning (ML) models to identify deviations that may indicate potential health risks. A prototype of our system has been developed to test its feasibility and demonstrate, through the application of two of the most frequently used ML models, i.e., isolation forest and Long Short-Term Memory (LSTM) networks, that it can provide scalability, efficiency, and reliability in the context of elderly care. Further, the provided dashboard enables caregivers and healthcare professionals to access real-time alerts and longitudinal trends, facilitating proactive interventions. The proposed approach improves healthcare responsiveness by providing instant insights into patient behavior, facilitating more accurate diagnoses and interventions. This study lays the groundwork for future advancements in the field and offers valuable insights for the research community to harness the full potential of combining edge computing, artificial intelligence (AI), and the IoT in elderly care.

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.

Research centers or universities daily produce interesting research products thanks to their ecosystem of laboratories and small spin-offs, but this ecosystem frequently has purely academic vision and does not easily meet the demand of the productive world. At the same time, the productive world is more aware of the market needs and, although it always looks for new solutions to be designed in collaboration with the academic world, it lacks the necessary contacts and connections. This paper presents a microservice-based software architecture designed to facilitate connections between researchers and any stakeholder interested in research outcomes while also accelerating the digital transition towards a more sustainable and smart world. Within the paper, the proposed system architecture is described and functionally validated as a first step in the development of such a similar solution and to provide a useful hint for future works to researchers and experts interested in its application.