Infants' spontaneous movements explore arm dynamics.

Learning to control the body is a fundamental process in human development. Before acquiring goal-directed skills such as walking or reaching, infants undergo developmental phases characterised by spontaneous movements with no apparent objective. These motions are believed to shape the sensorimotor system by facilitating body-environment interaction. However, how this exploration contributes to sensorimotor structuring remains an open question. A major challenge in studying spontaneous movements has been the lack of appropriate comparative data. To address this, we introduce a synthetic data-driven approach to analyse infant motion. We analysed 20 infants comprising 270 spontaneous movement units from 12 RGB-D infant recordings (22.5 ± 5.96 movement units per infant) together with 206 units from 8 RGB YouTube recordings (25.75 ± 6.34 movement units per infant), and compared these empirical datasets against two synthetic datasets. Our analysis revealed that spontaneous movements, both at the infant and cluster level, engaged arm dynamics more extensively than reaching-like motions and displayed acceleration distributions skewed towards trajectories optimised for maximal dynamic excitation. Furthermore, the kinematic space explored by infants exhibited significantly higher variability. These findings demonstrate that spontaneous movements are dynamically rich, providing emergent features potentially helpful for infants to explore movement possibilities and develop coordination and control.