AI-based Model for Rehabilitation Engagement and Motor Performance Evaluation in Pediatric Patients
AI-based Model for Rehabilitation Engagement and Motor Performance Evaluation in Pediatric Patients
What is the purpose of this study? This study wants to learn how the physiological signals and movement data can help researchers understand and predict how children with neuromotor impairments engage with rehabilitation.
Who can take part? 40 participants between 5 and 17 years old will take part. Half of them will be children with neurotypical development (control). The other half will be children with neuromotor impairments.
What will happen in the study? Children within the control group will take part in 1 rehabilitation session, while those with neuromotor impairments will take part in 2 to 3 rehabilitation sessions as part of the treatment plan already provided by their referring physicians.
During each session, researchers will collect: (i) physiological signals like heart activity (ECG), heart rate variability (HRV), and electrodermal activity (EDA); (ii) movement data; (iii) exergames scores and motor performance data; (iv) questionnaires filled out by therapists to understand how engaged the participant is.
Children will use one of two technology-assisted rehabilitation systems during their sessions: either the Lokomat (robotic exoskeleton that supports children with impairments in walking) or the GRAIL system (a treadmill system with motion tracking and extended reality).
Why is this study important? Researchers will use the data to understand and predict how each child responds to therapy and engage with it. These analyses will help therapists adjust rehabilitation settings in real-time, offering more personalized and effective care for children with neuromotor impairments.
Inclusion Criteria:
Exclusion Criteria: