A Multi-Layer architecture for Human Digital Twin

The concept of the digital twin, initially applied in industry, has recently made significant advances in healthcare, giving rise to the Human Digital Twin (HDT). This emerging technology has the potential to transform healthcare by creating virtual replicas of individuals, enabling real-time monitoring and simulation of their physiological states. HDTs integrate data from wearable sensors and other IoT devices, harnessing the power of artificial intelligence to support highly personalized healthcare services. These digital counterparts allow healthcare providers to make more informed decisions, predict health outcomes, and tailor treatments to individual needs. The development of HDTs paves the way for preventative care, chronic disease management, and continuous health monitoring representing a paradigm shift towards more proactive and patient-centered healthcare. In this paper, we propose a multi-layer architecture for Digital Twin systems that enables the seamless integration of Machine Learning (ML) and Deep Learning (DL) models. The architecture is designed to dynamically adapt to the available data sources, selecting and requesting the most appropriate model for execution based on the specific context. Our solution consists of three layers: A smartphone application that acts as a context-aware data collection platform, gathering inputs from a diverse array of sensors and querying cloud services to retrieve the most suitable ML model for the collected data. A cloud layer that serves as a repository for ML and DL models, responsible for identifying and delivering the optimal model based on the real-time sensor data and context. A fog layer, where a local node is used for data offloading, executing resource-intensive algorithms, and supporting long-term storage. This architecture achieves a balance between performance, scalability, and user privacy, providing an efficient framework for digital twin applications in personalized healthcare.