Carlo Campagnoli (University of Leeds) – Hidden patterns in reach-to-grasp movements: using kinematics and machine learning to reveal perceptual strategies

Visually guided actions such as reaching and grasping, though seemingly simple, encode a wealth of information about how the three-dimensional world is represented to guide movement. The kinematics of these actions reveal “perceptual signatures” that correlate with object properties such as size, distance and orientation, as well as with moment-to-moment adaptations in motor learning. Beyond these physical mappings, kinematic patterns also reflect individual styles of grasping, i.e. distinct strategies for balancing uncertainty and efficiency under the same movement goal.

Recent work has shown that these kinematic patterns can be mined not only for biomechanical information but also for the latent cognitive and perceptual variables that shape movement. Last year, in collaboration with a student from the School of Mathematics, we began applying machine-learning methods to 3D motion-capture data to classify grip strategies and identify these hidden attractors within the data. The initial results are promising and open exciting possibilities for further cross-disciplinary development.