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.
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.
Recent orthopedic or neurosurgical interventions (<6 months).
Uncontrolled seizures affecting gait.
Non-ambulatory (GMFCS IV-V) or cognitive impairments preventing cooperation.
Children with cerebral palsy classified within Gross Motor Function Classification System (GMFCS) Levels I-III who are ambulatory with or without assistive devices and receiving routine physiotherapy rehabilitation. Participants from all cerebral palsy subtypes will be included for clinical, functional, and gait assessment related to AI-based evaluation of functional mobility and gait outcomes.
Cohort
Prospective
AI-Based Functional Mobility and Gait Assessment
Other interventionParticipants will continue receiving their standard/routine physiotherapy rehabilitation program as prescribed by their treating therapist. The study will involve observational collection of clinical, functional, and gait-related data using standardized assessment tools, and AI-based video analysis. No additional therapeutic intervention will be administered specifically for research purposes.
GMFM-88
GMFM (Gross Motor Function Measure) Reliability: Excellent. Internal consistency Cronbach's α \~0.997-1.00; intra- and inter-rater ICC \~0.994-0.999 (both GMFM-88 \& GMFM-66) Validity: Construct and concurrent validity supported by strong correlations with related motor function classifications (e.g., GMFCS, PEDI mobility)
Markerless Gait Analysis
Gait videos will be processed using a validated markerless pose estimation framework (MediaPipe) Spatiotemporal and kinematic gait parameters will be extracted, including but not limited to: * Step length symmetry * Cadence * Stride time variability * Joint angle trajectories * Temporal asymmetry indices
Edinburgh visual gait scale (EVGS)
Edinburgh visual gait scale (EVGS) EVGS can be a supportive tool that adds quantitative data instead of only qualitative assessment to a video only gait evaluation. Interobserver agreement is 60-90% and Kappa values are 0.18-0.85 for the 17 items in EVGS. Reliability is higher for distal segments (foot/ankle/knee 63-90%; trunk/pelvis/hip 60-76%). Agreement between EVGS and 3DGA is 52-73%.
WeeFIM (Functional Independence Measure for Children)
WeeFIM (Functional Independence Measure for Children) Reliability: High internal consistency and ICCs (motor and cognitive scales) \~0.91-0.98 in children with cerebral palsy Validity: Construct and external validity supported (scale fits Rasch model expectations and correlates with related developmental measures)
System usabiity scale (SUS)
10 items likert scale questionnaire evaluating percieved usability and acceptability
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