Dear Colleagues,

The integration of embedded/cyberphysical systems and AI/ML advancements in health monitoring represents an emerging domain and a rapidly expanding multifaceted research area with vast societal and economic impacts. In this context, a wide range of ICT domains ranging from reliable hardware, embedded systems, multimodal sensors, wireless sensor communication, cloud computing, machine learning and AI algorithms, to security, robotics and many others are key enablers of Health 4.0 environments. This is also emphasized by the continuously expanding application domains where accurate, real-time, and multimodal health monitoring is of paramount importance including, but to limited to, health institutions, home environments, working environments, industry, smart city environments, and automotive environments, effectively mandating new healthcare paradigms and approaches. However, the wide range of involved heterogenous technological domains combined with the pressing need for added value services forms a nexus of diverse and potentially orthogonal challenges, which is imperative to tackle. In this framework, the Special Issue “Embedded Systems for AI-based Health Monitoring in Cyber Physical Systems” aims to promote the most recent technical advances in all relevant aspects, including theory, tools, applications, systems, testbeds, and in-the-field deployments. Both the theoretical derivations or practical development of AI-based health monitoring CPSs and testbeds are welcomed. Reviews and surveys of the state of the art in the respective systems are also welcome. Topics of interest to this Special Issue include, but are not limited to, the following topics:

AI-based health monitoring system architecture;
Control optimization of sensors and robots in next generation health monitoring CPSs;
Machine learning and AI algorithms in health monitoring;
Data mining and analytics;
Model-based design and verification of embedded systems in demanding health monitoring environments;
New low-power platforms and sensors for health monitoring;
Mobile and cloud computing for robotic systems in AI/ML-based health monitoring environments leveraging embedded systems;
Wired and wireless communication technologies in AI-based health monitoring;
Performance/power optimization through hardware accelerators and components;
Signal processing and fusion for complex AI/ML-based health monitoring environments;
Practical application-oriented system design for embedded systems in AI/ML health monitoring;
Security and privacy for next generation AI/ML-based health monitoring.

Dr. Christos Antonopoulos
Prof. Dr. Nikolaos Voros
Dr. Georgios Keramidas
Dr. Aris Lalos
Guest Editors