Davide di Febbo

DavideBioFoto
Dr. Davide Di Febbo

Post-Doc

 

Contacts:

davide.difebbo@polimi.it

 

     

Davide Di Febbo obtained a Master’s Degree in Bioengineering in Sep 2017 and completed his PhD in Biomedical Engineering in Jul 2021 at NearLab, Politecnico Di Milano. During his PhD studentship, under the supervision of professor Simona Ferrante, he worked in the fields of eHealth and remote monitoring within the European project MOVECARE (H2020-ICT-26b-2016, GA 73215), aimed at developing a home monitoring/assistance system for community-dwelling elders. The system included sensorized daily-use objects for the activities of daily-life monitoring, digital neuropsychological tests and an AI engine to predict an early decline in uncontrolled settings. He worked in the design and testing of a novel mobile device for the remote assessment of free handwriting in older adults and he studied handwriting data analysis to detect signs of a pathological ageing process in uncontrolled settings. He also developed a retrospective knowledge-based decision support system for the clinical evaluation of the Rey-Osterrieth complex figure test for the diagnosis of mild cognitive impairment and dementia.

After his Master Degree, he spent six months as a research fellow in rehabilitation engineering at NearLab in the European project RETRAINER (H2020-ICT-2014-1), where he continued his master thesis work on the design of an advanced control system based on reinforcement learning for a functional electrical stimulation-based neuroprosthetics for post-stroke rehabilitation, under the supervision of professors Simona Ferrante, Marcello Restelli, Emilia Ambrosini and Alessandra Pedrocchi.

Now he is working as a PostDoc researcher in the European project ESSENCE (H2020, GA 101016112) on the development of a home monitoring system for older adults and children in the pre-school age which integrates mobile devices to acquire user’s data and remote platforms for clinical testing and teleconsultation. His activities are mainly focused on cloud AI-based algorithms to detect anomalies in longitudinal user’s data for the early detection of age-decline and learning problems.

His research interest is about the exploitation of artificial intelligence (AI) in healthcare for the development of remote monitoring systems and clinical expert systems. Moreover, he is interested in explainable AI solutions to enhance the interpretability of machine learning-based eHealth applications.

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