Rehabilitation Assessment of Motor Function In Cerebral Palsy Using Explainable AI

The goal of this observational study is to develop and validate an AI-based prediction model for functional mobility and gait outcomes in children with cerebral palsy using low-cost clinical and gait data collected in rehabilitation settings in Pakistan. The study aims to determine whether machine learning models can accurately predict mobility status, gait symmetry, and functional independence in ambulatory and non-ambulatory children with cerebral palsy. The main questions it aims to answer are: * Can clinical and gait-related variables accurately predict functional mobility and gait outcomes in children with spastic cerebral palsy? * Can video-based assessment tools provide clinically useful data for AI-based rehabilitation assessment in low-resource settings? Researchers will analyze clinical, functional, and gait data to identify patterns associated with mobility limitations and rehabilitation outcomes. Participants will: * Undergo clinical and functional assessments, including measures of balance, mobility, posture, and functional independence. * Perform gait and movement tasks while data are collected using AI-based video analysis tools. * Participate in routine rehabilitation sessions while their movement and functional performance are recorded for analysis. * Provide demographic and clinical information relevant to cerebral palsy severity and functional status.

Inclusion Criteria: * Age 4 to18 years * Diagnosed any motor type of cerebral palsy (spastic, dyskinetic, ataxic, mixed),) * GMFCS levels I -III (able to walk with or without an assistive device). * All participants must be able to ambulate at least 10 meters with or without an assistive device. * Capable of following simple verbal instructions. * Parental informed consent and child assent Exclusion Criteria: * Recent orthopedic or neurosurgical interventions (\