InfantMotion2Vec: Unlabeled Data-Driven Infant Pose Estimation Using a Single Chest IMU

Mohammad Nur Hossain Khan, Nancy L. McElwain, Mark Hasegawa-Johnson, Bashima Islam

Abstract: Early identification of neuro-developmental risks in infants is crucial for timely intervention and improved quality of life. Current screening methods are costly, intrusive, and limited by artificial environments or require the infant to wear multiple sensors. To address these challenges, we propose a novel approach leveraging inertial measurement units (IMUs) to monitor infants' spontaneous motor abilities in natural settings. Our method introduces a hierarchical semi-supervised classifier and the InfantMotion2Vec embedding to capture detailed motion patterns, accommodating a wide age range (up to 36 months) while minimizing reliance on labeled data and cumbersome sensor setups. We collected labeled IMU data from 25 families and unlabeled data from 42 families using a single wearable sensor. Pretraining an embedding network using unlabeled data with a hierarchical pose estimator resulted in a 26% increase in F1-score and a 77.7% increase in Cohen's Kappa score compared to using only labeled data. The InfantMotion2Vec embedding adequately handles highly unbalanced labeled data, demonstrating its effectiveness in infant posture classification.