The analysis of characteristic motions in infants plays a pivotal role in quantifying developmental progress and clinical risk for neurodevelopmental and musculoskeletal abnormalities. Traditional methods often rely on resource intensivemanual motion assessments carried out by clinicians while computer assisted approaches frequently utilize computationally expensive simulations or black-boxclassification models. These approaches struggle to efficiently to both captureand differentiate the highly correlated dynamics of infant motion, limiting theirability to deliver actionable insights in a clinically viable decision time frame. Inresponse to these challenges, we introduce the use of Dynamic Mode Decomposition (DMD) as a transformative approach for decomposing complex infant motioninto interpretable, independent components that are linearly additive in nature.DMD not only enables extraction of large scale clinically meaningful patternsbut also can integrate with existing computer assisted interventions with regardto standardized motion features. We assess an optimized DMD formulation on275,000 frames of infant motion in clinical settings that have undergone manualmotion assessment by clinicians. Our experimental results show that using DMDmodes as predictive components not only result in equal or superior accuracy inpredicting abnormal clinical motion assessments compared to traditional manualor computer assisted methods but serve as highly data rich features themselvesthat can be used as a novel basis for personalized clinical analysis and uncertaintyquantification at scale.