Maintaining motivation and sustained engagement in pediatric neurorehabilitation remains a significant challenge, particularly for children with neuromotor impairments. Traditional therapy methods often lack personalized adaptability, which can limit adherence and effectiveness. The proposed framework addresses this gap by integrating multimodal sensor data, including EEG, posture, and gaze, to continuously monitor emotional, cognitive, and behavioral engagement during therapy sessions. This real-time assessment enables dynamic adaptation of game-based exercises with the aim of optimizing motivation, reducing disengagement, and promoting functional recovery. This concept was implemented in a clinical setting with children diagnosed with coordination disorders and neuropsychomotor delays. Preliminary results with four participants indicate that by tracking engagement levels and supporting session personalization, it is possible to stimulate the child’s motivation across multiple sessions. These findings suggest that incorporating adaptive, engagement-driven frameworks can provide a useful tool to improve rehabilitation efficacy, offering a way toward more personalized and responsive therapeutic strategies in pediatric neurorehabilitation.
ACM CHI Conference on Human Factors in Computing Systems