Kinematic and Muscle Synergies in Grasping Hand

Abstract

The human hand is a multidimensional robot that can execute complex functions withincredible ease. It is hypothesized that the central nervous system (CNS) achieves this by controlling in low dimensional space of movement primitives also known as synergies. Kinematic and muscle synergies have been extensively studied, but only a few studies have focused on both. In this theses, kinematic and muscle synergies were extracted from the hand grasps often used in the activities of daily living using principal component analysis (PCA). Movements were then reconstructed as weighted linear combinations of synergies. The results indicated that optimal command signals originating from the CNS to achieve a successful grasp might be reflected in muscles and kinematics. Also, to understand the interplay between kinematic and muscle synergies, musculoskeletal synergies were extracted using PCA and data fusion methods. The research findings and methods presented here might enable improved control of neuro-prosthetics.