Vision-based human grasp reconstruction inspired by hand postural synergies

The human hand exhibits enormous versatility and dexterity, to a degree paralleled by few gripping assemblies. Although several hand posture animators have been discovered, vision-based trackers have retained the research focus owing to their compactness, cost-effectiveness and ease-of-installation. The present investigation explores a marker-based hand pose-tracking solution, using a Kinect depth-capture device. It exploits the inherent synergism within the finger linkages through a novel motion capture algorithm for grasp reclamation. The tracked data-set is analysed for an optimal number of condensed primitives which yielded an effectively reclaimed grasp-pose. Isomap based dimensional reduction followed by Principal Component Analysis (PCA) back-projection, drives the reconstruction of thirty-three Feix-grasps solely from Index, Thumb and three edges of a Palm marker. The derivatives were observed to contribute across grasp initiation to final posture assumption, with a scope for future investigations on their direct correlations to cortical motor impulses.