Sede ufficiale: VIA G. CARUSO, 16, 56122 PISA
Email: alessandro.tognetti@unipi.it
Telefono: 050 2218252
We promote the dissemination of knowledge and support social transformation, contributing to the progress of both the community and the region
Sede ufficiale: VIA G. CARUSO, 16, 56122 PISA
Email: alessandro.tognetti@unipi.it
Telefono: 050 2218252
Struttura: Dipartimento di Ingegneria dell'Informazione
Settore scientifico-disciplinare: Bioingegneria IBIO-01/A
Modalità: E' richiesta una conferma preventiva per mail.
Luogo: Ufficio del Docente (sede DII ultimo piano DICI sede di ingegneria strutturale)
Orario: Ricevimento: Mercoledì 9-11 Tutoraggio: Mercoledì 11-12.30
The research activity focuses on the development of non-invasive biomedical technologies and the computational modeling of physiological systems, with particular attention to cardiac electrophysiology.
Non-Invasive Biomedical Technologies
This research line addresses the design and development of devices and systems for the continuous and non-invasive monitoring of physiological parameters in real-world conditions. Activities include:
Design and modelling of wearable and unobtrusive sensors for the detection of posture, movement, cardiorespiratory signals, and other physiological parameters.
Integration of embedded systems for data acquisition and processing in uncontrolled environments.
Development of artificial intelligence algorithms for the processing of physiological signals and the classification of clinical or functional states.
These technologies are applied in healthcare and clinical settings, with particular focus on daily patient monitoring and predictive, personalized medicine.
Computational Modeling of Physiological Systems
The second research line is dedicated to computational modeling, with a specific focus on the heart and cardiac electrophysiology. Research activities in this area include:
Development of numerical models for the simulation of cardiac electrical activity.
Digital Twin approaches to build virtual, personalized representations of the heart based on clinical data and simulations.
Application of these models to better understand pathophysiological mechanisms and to support diagnosis and therapy planning.
Work in both domains aims to provide practical tools for digital medicine, combining advanced measurement technologies with personalized models to improve monitoring, prevention, and clinical decision-making.