Serena Moscato

Digital Biomarkers for Pain Assessment in Multiple Sclerosis patients
(in collaboration with Neurological Science Institute of Bologna - Ospedale Bellaria)

• Development of automatic methods to assess pain based on wearable sensors' physiological signals (i.e., to assess intensity of pain, presence/absence of pain, type of pain)
• Exploring the relationship between pain and physical activity
• Pain and Sleep: Wearable Sensor Signal Analysis for Insightful Exploration

REMEDY: Remote modular monitoring for non-communicable diseases management and recovery
(in collaboration with IRCCS S. Orsola)

• Systematic state-of-the-art evaluation of cardiorespiratory monitoring systems
• Management of technical documentation for IP protection and medical device certification
• Advanced signal processing for cardiorespiratory monitoring

Foundation models on EEG signals to assess neuropathic pain (thesis abroad in collaboration with Malmö University, Sweden):
Recent unsupervised machine learning techniques have allowed training “foundation” models on millions of EEG signals to extract features that are representative of the complex and varying singal morphology across individuals and health conditions. In this project, you will apply existing foundation models to a dataset collected from spinal cord injury patients, with the objective to quantify neuropathic pain. The aim is to provide objective, quantitative means to better adjust pain medications and even predict insurgence of pain. The project is in collaboration with Malmö University, Sweden, Universidad de Sevilla, Spain and Hospital Nacional de Paraplegicos, Toledo.

Look of Life 2.0 (LoL2.0): Virtual Reality for advanced cancer patients assisted at home: A randomized controlled interventional study (in collaboration with Fondazione ANT)

• To analyse physiological signal recordings in real-world in relation to cancer-related psychophysical symptomatology

• To develop machine learning models to reliably assess presence or absence of psychophysical symptomatology

• Assessing the relationship between opioid use and physiological signals

Data management in clinical research
(multiple international projects)

• Semantic standardization of research data using SNOMED-CT and HL7 FHIR