The field of movement analysis and modeling is experiencing a significant shift towards a more integrated understanding of behavior and intelligence. Researchers are moving beyond traditional approaches that focus on specific domains or tasks, and instead, are developing more generalizable and interpretable models that capture the underlying structure of movement. This new direction is driven by the recognition that movement is a fundamental aspect of biological systems and a primary target for AI modeling. By treating movement as a rich and structured modality, researchers can unlock new insights into behavior, prediction, and interaction across species and settings. Noteworthy papers in this area include:

• One study that presents a framework for uncovering sensorimotor relationships and discovering motion primitives from high-dimensional motor and sensory information.
• Another study that explores the use of 3D pose tracking data to improve our understanding of dribbling skills in soccer.
• A method for formation recognition and player position assignment in football using template matching and linear assignment.
• A seminal paper that argues for treating movement as a primary modeling target for AI, highlighting its significance in interpreting behavior, predicting intent, and enabling interaction.