AI-based Model for Rehabilitation Engagement and Motor Performance Evaluation in Pediatric Patients: A Pilot Study

AI-based Model for Rehabilitation Engagement and Motor Performance Evaluation in Pediatric Patients

What is the purpose of this study? This study aims to evaluate the usability and feasibility of an artificial intelligence-based model designed to monitor in real-time the engagement and motor performance of pediatric patients during technology-assisted rehabilitation.

Who can take part? 15 participants between 5 and 17 years old with neuromotor impairments will take part, along with at least 5 of their referring physiotherapists.

What will happen in the study? Each pediatric patient will take part in a single, 1-hour rehabilitation session using either the Lokomat or GRAIL system, according to their standard clinical prescription. During the session, the physiotherapist will have access to a display showing real-time data from the AI model, including the patient's heart rate, engagement level, pleasantness, activation, and motor performance. At the end of the session, the physiotherapist will complete a System Usability Scale (SUS) questionnaire and provide direct feedback on how to improve the model.

Why is this study important? Assessing the usability of this real-time monitoring tool is a necessary step to understand if it is practical for clinical use. Providing therapists with objective, real-time insights into a child's psychological and physical state can ultimately help tailor therapy to the specific needs of each patient, improving the overall rehabilitation experience.

Inclusion Criteria:

• Subjects aged between 5 and 17 years with neuromotor impairments who are undergoing rehabilitation therapy using the Lokomat and GRAIL devices, according to the existing clinical plan.

Exclusion Criteria:

• Uncooperative subjects.