Fit4MedRob

Fit4Medical Robotics is a national research initiative funded by the Italian Ministry of University and Research. The project aims to address current knowledge gaps in the field of rehabilitative robotics through a multidisciplinary approach

Description

Fit4Medical Robotics is a national research initiative funded by the Italian Ministry of University and Research. The project aims to address current knowledge gaps in the field of rehabilitative robotics through a methodologically rigorous and multidisciplinary approach. In addition to scientifically assessing the clinical effectiveness of Robotic Assistive and Diagnostic Technologies (RADTs), the initiative also focuses on systematically evaluating their economic and organizational sustainability, with the ultimate goal of supporting their appropriate and informed integration into the National Health System.

Professor Silvana Quaglini serves as Group Leader of Mission 1 of the project, alongside Dr. Irene Aprile (Fondazione Don Gnocchi). This mission is dedicated to the implementation of pragmatic clinical trials to evaluate the effectiveness of commercially available robotic rehabilitation solutions.

Within our laboratory, we are engaged in the following activities:

  • design and management of multiple REDCap repositories for standardized clinical data collection from four pragmatic trials involving patients with stroke or neurodegenerative diseases;

  • analysis of the clinical data collected during the trials;

  • continuous monitoring of trial progress;

  • development of generative systems for the simulation of rehabilitative movements;

  • design of machine learning and deep learning models for Human Activity Recognition (HAR);

  • development of clinical ontologies for the semantic encoding of validated medical assessment scales.

Manager

Silvana Quaglini

Professore Ordinario - BMI

Participants

Giovanna Nicora

Ricercatore - BMI

Silvana Quaglini

Professore Ordinario - BMI

Publications

Nicora, Pe, Santangelo, et al.

Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions

Published in:  Journal of NeuroEngineering and Rehabilitation

Nicora, Parimbelli, Quaglini et al.

Healthcare practitioners and robotic-assisted rehabilitation: understanding needs and barriers

Published in:  Journal of NeuroEngineering and Rehabilitation

Santangelo, Nicora, Bellazzi, Dagliati

How good is your synthetic data? SynthRO, a dashboard to evaluate and benchmark synthetic tabular data

Fasano et al

Towards the Identification of Patients’ Needs for Promoting Robotics and Allied Digital Technologies in Rehabilitation: A Systematic Review

Published in:  Healthcare