Multi-constrained inverse kinematics for the human hand

Measuring the spatial and temporal characteristics of hand movement is a challenging task due to the large number of degrees of freedom (DOF) in the hand. This paper presents a multi-constrained inverse kinematics (IK) approach for hand motion estimation from motion capture data. The IK approach satisfies a set of prioritized motion and postural constraints for each hand joint and link. The high-priority constraint is fully satisfied, while the fulfilment of the low-priority constraints is achieved as long as no conflict with the high-priority constraint exists. The proposed approach can aid marker-based motion capture technologies in accurately reconstructing discontinuities or erroneous marker trajectory segments resulting from occluded, missing, or flipped markers. The performance of the multi-constrained IK approach for the hand is tested for a full range of continuous hand motion.