Investigating the integration of gamification elements in mobile health applications to enhance user engagement and therapeutic outcomes. This research focuses on developing evidence-based game mechanics that motivate continued participation in motor skill rehabilitation programs.

Pattern mining in spatiotemporal movement trajectory (SMT) data is essential to understand the motor skill learning and adaptation strategies that inform neurorehabilitation practices. Movement performance metrics (i.e., speed, accuracy) are insufficient to characterize motor control strategies and learning patterns, particularly in individuals with disordered movement. Motor skill learning patterns require an interpretable sequential SMT representation that preserves spatial, temporal, and performance variables. We present a novel spatiotemporal contrastive representation learning (STCRL) framework that combines transformer-based trajectory embedding with multi-temporal contrastive learning, enabling the decoding of movement patterns and control strategies in human and non-human movement data. STCRL encodes 3D SMT into a high-dimensional latent space by preserving spatial and temporal dependencies while enabling cross-task and cross-subject knowledge transfer. We introduce an Exploration-Exploitation (E-E) analytical framework that quantifies skill learning and control strategies to balance different movement patterns and micro-adaptation. We tested and validated the STCRL with two visuomotor reaching datasets: (1) a prospectively obtained cohort of term and preterm children's motor learning and performance of unimanual and bimanual tasks, and (2) extensively overtrained non-human primates performing target-directed reaching movements. Our findings were that E-E patterns significantly correlated with the early and late phases of motor learning patterns and speed-accuracy trade-offs principles, as previously documented. STCRL framework provides an efficient computational approach for quantifying motor learning strategies with potential applications in developmental assessment, rehabilitation monitoring, and movement optimization in robotics or brain-computer interfacing research.