THEMATIC SESSION #33

Micaela Morettini

Marche Polytechnic University (UnivPM), Italy

• m.morettini@univpm.it

Ilaria Marcantoni

Marche Polytechnic University (UnivPM), Italy

• i.marcantoni@staff.univpm.it

Agnese Piersanti

Marche Polytechnic University (UnivPM), Italy

• a.piersanti@staff.univpm.it

This thematic session addresses digital phenotyping as an engineering framework for quantitatively linking behavior, physiology, and health through sensor-based measurements. The focus is on the acquisition, modeling, and interpretation of data derived from both unimodal and multimodal sensing modalities, spanning from advanced physiological sensing technologies to scalable sensing approaches. The session will highlight approaches that bridge raw sensor measurements and physiological understanding, supporting applications such as early disease detection, personalized health monitoring, and modeling of physiological interactions. Contributions presenting innovative methodologies, validation strategies, and real-world applications are encouraged. Emphasis is placed on methods that enhance robustness, interpretability, and generalizability, enabling reliable inference of health-relevant states in both laboratory and ecological settings.

The session also acknowledges that issues related to signal quality, data heterogeneity, inter-individual variability, missing data, and limited clinical interpretability currently constrain the translation of digital phenotyping into actionable health applications. Accordingly, contributions are encouraged to address these challenges through advances in sensor design, signal processing, computational modeling, and machine learning approaches grounded in physiological knowledge.

Topics of interest include (but are not limited to):

• Digital phenotyping methodologies for behavioral and physiological monitoring;
• Multimodal sensor fusion and feature extraction from wearable and mobile data;
• Biomedical signal processing for ambulatory and real-world sensing;
• Computational modeling of behavior–physiology–health interactions;
• Machine learning approaches for physiological inference from passive sensing;
• Validation of digital phenotypes against clinical and physiological benchmarks;
• Digital biomarkers for cognitive, mental, metabolic, and cardiovascular health;
• Longitudinal modeling and personalized health trajectories;
• Digital phenotyping for chronic disease management and prevention;
• Digital phenotyping in neural engineering, brain–computer interface systems, and human–AI interaction;
• Reliability, robustness, and reproducibility of digital biomarkers in real-world deployment;
• Digital phenotyping in XR environments;
• Ethical, privacy, and fairness considerations in digital phenotyping;
• Integration of digital phenotyping into clinical workflows and decision support.

Micaela Morettini holds a M.sc. in Biomedical Engineering (University of Bologna, 2008) and a PhD in “Electromagnetics and Bioengineering” (UnivPM, 2012). She is Associate Professor of Bioengineering at the Department of Information Engineering, UnivPM, where she is in charge of the DIABETES LAB. Her research interests include: in-silico modelling and digital twin technologies in physiology and medicine, wearable devices for health monitoring and disease management, biomedical signal processing, digital health and digital biomarkers, machine and deep learning applied to the development of clinical decision support systems. Main applications are in the diabetes, metabolism/immunometabolism, physical exercise, cardiovascular and respiratory fields. She currently is the coordinator of the Bachelor's and the Master's Degree in Biomedical Engineering at UnivPM. She is author of 77 journal papers and 80 conference proceedings.

Ilaria Marcantoni is Assistant Professor at the Department of Information Engineering, UnivPM. She received her PhD in Information Engineering – curriculum: Biomedical, Electronic and Telecommunication Engineering – from Università Politecnica delle Marche (UnivPM) in 2021. Her main research interest is the automatic processing of brain signals (in particular electroencephalogram) and images (in particular magnetic resonance images) for the study of structural and functional cortical connectivity, as well as of cortical activation maps while performing a task or receiving stimuli. She also works on the automatic processing of digital cardiovascular signals, particularly for the detection of non-invasive cardiovascular risk indexes. She is the author of more than 70 papers, including journal articles and conference proceedings.

Agnese Piersanti got M.sc. in Biomedical Engineering in 2020, and PhD in Information Engineering at the Department of Information Engineering of UnivPM, in 2024, defending a thesis titled “Digital health technologies to improve diabetes prevention and optimize therapy: from model based approaches to feature based machine learning”. During the PhD, she spent an Erasmus+Traineeship period at the University of Southern California (Los Angeles, CA), for the development of an in-silico modeling approach to quantify insulin bioavailability. She had a one-year Post-Doc Fellowship on glucometabolic data analysis in gestational diabetes at the CNR Institute of Neuroscience (Padova, Italy). Currently, she is a Post-Doc Fellow at UnivPM, and her main research interests involve the development of bioengineering methodologies and low computational impact artificial intelligence algorithms for the study, management and treatment of major chronic diseases, such as diabetes.