DIGITAL REHABILITATION PLATFORM FOR CHILDREN WITH DISABILITIES

Children with disabilities often require long-term, multidisciplinary rehabilitation involving medical, psychological, educational, and social interventions. At the same time, their mothers or primary caregivers experience continuous physical, emotional, informational, and organizational burdens. Although existing pediatric telerehabilitation systems have improved access to rehabilitation services, current approaches are largely fragmented, focusing primarily on child-centered outcomes while insufficiently addressing caregiver needs within unified decision-making frameworks. This study proposes a digital rehabilitation platform for children with disabilities that adopts a family-centered approach by modeling the child–caregiver dyad as an integrated and dynamic system. The platform supports personalized rehabilitation planning, prioritization of intervention modules, and adaptive adjustment of rehabilitation intensity through an intelligent decision-support mechanism. The proposed system integrates multiple dimensions, including child functional status based on the International Classification of Functioning, Disability and Health (ICF), caregiver burden and self-efficacy, treatment adherence, environmental accessibility, and safety constraints. A constrained multi-criteria optimization model is used to recommend rehabilitation activities by balancing expected child improvement, caregiver well-being, adherence probability, resource limitations, and digital accessibility costs. To ensure adaptability over time, a fuzzy logic–based control module continuously updates rehabilitation plans based on progress indicators, adherence levels, caregiver burden, and safety signals. All significant modifications require validation by healthcare professionals, ensuring clinical safety and human-in-the-loop governance. These findings indicate that a dyadic, family-centered digital rehabilitation platform can effectively integrate child and caregiver needs into a unified adaptive framework. The proposed model provides a strong foundation for future clinical validation, usability testing, and real-world deployment, particularly in contexts where rehabilitation resources are limited and continuity of care is challenging.