Linear and Nonlinear Kinematic Synergies in the Grasping Hand

Abstract

Kinematic synergies in human hand movements have shown promising applications in dexterous control of robotic and prosthetic hands. We and others have previously derived kinematic synergies from human hand grasping movements using a widely used linear dimensionality reduction method, Principal Component Analysis (PCA). As the human biomechanical system is inherently nonlinear, using nonlinear dimensionality reduction methods to derive kinematic synergies might be expected to improve the representation of human hand movements in reduced dimensions. In this paper, we derived linear and nonlinear kinematic synergies from linear (PCA), globally nonlinear (Isomap, Stochastic Proximity Embedding (SPE), Sammon Mapping (SaM), and Stochastic Neighbor Embedding (SNE)) and locally nonlinear (Local Linear Embedding (LLE), LaplacianEigenmaps (LaE), and Local Tangent Space Alignment (LTSA)) dimensionality reduction methods. Synergies derived from linear PCA and nonlinear SaMwere able to capture multiple functional postures and physiological patterns. Results from natural hand grasping movements indicated that PCA performed better than all nonlinear dimensionality reduction methods used in the paper. Results from ASL postural movements indicated that PCA, SaM, and SPE better generalized over ASL postural movements when compared to other methods. Overall, our results show that PCA derived synergies offer qualitative and quantitative advantages over nonlinear methods as a limited number of kinematic synergies begin to be implemented in human prosthetics.