The research activity of B3Lab covers a wide range of topics. The lab has a long lasting expertise in biomedical signal processing, cardiovascular and cardiorespiratory modeling, modeling of neurosensorial and cognitive mechanisms and modeling of physiological systems, such as the cardiovascular autonomic nervous system (ANS) regulatory mechanisms. The expertise in signal processing comprises a large variety of methods and approaches among these, non-linear methods and analysis of complexity. B3lab is also involved in studies on the effect of microgravity on human physiology and relevant countermeasures (data from ESA parabolic flight, bed rest, …)

B3lab collaborates with several hospitals and clinics that provide data for medical image reconstruction and image processing for different purposes, such as for cancer classification, prediction of prosthetic loosening, pathophysiological understanding of neurodegenerative pathologies, non invasive quantification of anatomical and functional parameters from cardiac images (echography, margnetic resonance, computed tomography,…). Several research projects are also related to brain connectivity and functional mapping.

Since years B3lab has been collaborating with several clinical research units, research institutes, and companies to perform data analyses, in particular, by means of artificial intelligence (AI), machine learning and deep learning approaches for patient risk stratification or for classification of pathological state (e.g. atrial fibrillation), for prediction of patient condition progress. The final goal is to develop a decision support system and to integrate different types of data for a multiscale analysis (from molecules to macroscopic measures, such ECG or arterial blood pressure) and in different contexts (radiomics, metabolomics, multi-omics).

B3lab has a long lasting expertise in bioinformatics and has several collaborations with the objective to improve the therapies and contribute to personalized and precision medicine. The lab is also involved in projects with several industrial and academic partners for the remote monitoring with wearable and/or wireless devices for telemedicine applications, it has been involved in projects to improve contactless monitoring system (e.g. video camera signals for heart rate estimation) and to obtain physiological parameters from inertial sensors embedded in common hardware (e.g. smartphone, virtual reality headset,..).