Cerebral palsy (CP) is a group of nonprogressive neuro-developmental conditions occurring in early childhood that causes movement disorders and physical disability. Measuring activity levels and gait patterns is an important aspect of CP rehabilitation programs. Traditionally, such programs utilize commercially available laboratory systems, which cannot to be utilized in community living. In this study, a novel, shoe-based, wearable sensor system (pediatric SmartShoe) was tested on 11 healthy children and 10 children with CP to validate its use for monitoring of physical activity and gait. Novel data processing techniques were developed to remove the effect of orthotics on the sensor signals. Machine learning models were developed to automatically classify the activities of daily living. The temporal gait parameters estimated from the SmartShoe data were compared against reference measurements on a GAITRite mat. A leave-one-out cross-validation method indicated a 95.3% average accuracy of activity classification (for sitting, standing, and walking) for children with CP and 96.2% for healthy children. Average relative errors in gait parameter estimation (gait cycle, stance, swing, and step time, % single support time on both lower extremities, along with cadence) ranged from 0.2% to 6.4% (standard deviation range = 1.4%-9.9%). These results suggest that the pediatric SmartShoe can accurately measure physical activity and gait of children with CP and can potentially be used for ambulatory monitoring.