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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.

We consider the bistable equation proposed by Rosenau to replace the Allen-Cahn equation in the case of large gradients. We discuss the bifurcation problem for stationary solutions of this equation on an interval as the diffusion coefficient and the length of the interval are varied, concentrating on classical solutions.

Recently, the need for more secure identity verification systems has driven researchers to explore other sources of biometrics. This includes iris patterns, palm print, hand geometry, facial recognition, and movement patterns (hand motion, gait, and eye movements). Identity verification systems may benefit from the complexity of human movement that integrates multiple levels of control (neural, muscular, and kinematic). Using principal component analysis, we extracted spatiotemporal hand synergies (movement synergies) from an object grasping dataset to explore their use as a potential biometric. These movement synergies are in the form of joint angular velocity profiles of 10 joints. We explored the effect of joint type, digit, number of objects, and grasp type. In its best configuration, movement synergies achieved an equal error rate of 8.19%. While movement synergies can be integrated into an identity verification system with motion capture ability, we also explored a camera-ready version of hand synergies—postural synergies. In this proof of concept system, postural synergies performed well, but only when specific postures were chosen. Based on these results, hand synergies show promise as a potential biometric that can be combined with other hand-based biometrics for improved security.

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