Reaching Motion Characterization Across Childhood via Augmented Reality Games

While performance in coordinated motor tasks has been shown to improve in children as they age, the characterization of children's movement strategies has been underexplored. In this work, we use upper-body motion data collected from an augmented reality reaching game, and show that short (13 second) sections of motion are are sufficient to reveal arm motion differences across child development. To explore what drives this trend, we characterize the movement patterns across different age groups by analyzing (1) directness of path, (2) maximum speed, and (3) progress towards the reaching target. We find that although maximum arm velocity decreases with age (p~=~0.02), their paths to goal are more direct (p~=~0.03), allowing for faster time to goal overall. We also find that older children exhibit more anticipatory reaching behavior, enabling more accurate goal-reaching (i.e. no overshooting) compared to younger children. The resulting analysis has potential to improve the realism of child-like digital characters and advance our understanding of motor skill development.