Canonical correlation analysis for muscle synergies organized by sensory-motor interactions in musculoskeletal arm movements

Synergy is a key concept for understanding smooth and dexterous body movements generated in biological systems. In this paper, we propose an approach based on canonical correlation analysis (CCA) for identifying synergies that capture coherences organized between motor activations and sensory signals in movements. Using a musculoskeletal planar arm model with muscle and joint redundancies, we show that synergies identified by CCA give more natural and tractable activation patterns in virtual controller space with lower dimensionality when the arm performs reaching and a 3D pole balancing movement tasks.