Animating a Synergy-Based Deformable Hand Avatar for Haptic Grasping

Synergy-based hand pose sensing: Reconstruction enhancement

Many applications in human–machine interfaces, information visualization, rehabilitation and entertainment require hand pose reconstruction systems that are both accurate and economic. Unfortunately, economically and ergonomically viable sensing gloves provide limited precision due to the imperfect and incomplete correspondence of sensing models with the anatomical degrees of freedom of the human hand, and because of measurement noise. This paper examines the problem of optimally estimating the posture of a human hand using non-ideal sensing gloves. The main idea is to maximize their performance by exploiting knowledge of how humans most frequently use their hands. To increase the accuracy of pose reconstruction without modifying the glove hardware — hence basically at no extra cost — we propose the collection, organization, and exploitation of information on the probabilistic distribution of human hand poses in common tasks. We discuss how a database of such a priori information can be built, represented in a hierarchy of correlation patterns or postural synergies, and fused with glove data in a consistent way, so as to provide good hand pose reconstruction in spite of insufficient and inaccurate sensing data. Simulations and experiments on a low-cost glove are reported which demonstrate the effectiveness of the proposed techniques.

Using postural synergies to animate a low-dimensional hand avatar in haptic simulation

A technique to animate a realistic hand avatar with 20 DoFs based on the biomechanics of the human hand is presented. The animation does not use any sensor glove or advanced tracker with markers. The proposed approach is based on the knowledge of a set of kinematic constraints on the model of the hand, referred to as postural synergies, which allows to represent the hand posture using a number of variables lower than the number of joints of the hand model. This low-dimensional set of parameters is estimated from direct measurement of the motion of thumb and index finger tracked using two haptic devices. A kinematic inversion algorithm has been developed, which takes synergies into account and estimates the kinematic configuration of the whole hand, i.e., also of the fingers whose end tips are not directly tracked by the two haptic devices. The hand skin is deformable and its deformation is computed using a linear vertex blending technique. The proposed synergy-based animation of the hand avatar involves only algebraic computations and is suitable for real-time implementation as required in haptics.