Body position classification using wearable sensors in infants with cerebral palsy.

Infants learn through everyday interactions with the physical and social environment. For infants with cerebral palsy (CP), motor impairments may disrupt everyday learning opportunities. How can we measure real-world motor behavior and learning opportunities in infants with CP? Machine learning models have been developed to quantify body position throughout a day in infants with typical development (TD) using wearable sensor data. However, these models have not been validated in infants with motor impairments. This study assessed the validity of body position classification using machine learning and sensors in infants with CP. Ten infants with CP (7-18 months; one session each) and 19 infants with TD (4-12 months; 45 sessions) wore four inertial sensors on their legs throughout a day. Ninety minutes were video recorded and manually scored for body position in five categories: supine, prone, sitting, standing, and held. Random forest classifiers were trained to predict body position from sensor-derived motion features. Models trained on datasets that varied in size (9, 45, 54 sessions) and group composition (CP, TD, CP and TD) were compared to determine the most accurate model for infants with CP. Larger training sets and training sets that included data from infants with CP were the most accurate; the final models achieved similar performance in CP and TD (86% and 89% accuracy), captured meaningful individual differences (ICCs = 0.682-0.999), and generated predictions that were correlated with motor skill assessments. Findings demonstrate that wearable sensors and machine learning can accurately classify real-world body position in infants with CP.