Experience based grasping gesture synthesis and data glove calibration

An experience based gesture database construction method is proposed to solve the two primary problems for current calibration routine as: the difficulty for ground-truth data gathering and the oversimplified calibration model. By considering the anatomic structure of human hand and sensor layout of the data glove, the method established an accurate human hand kinematics model, which can realize flexible thumb movement and soft palm effect. Given the 3D object to grasp and the desired grasp task, an optimization procedure, combined with daily experience for grasping, was proposed for hand gesture synthesis. Least square regression and closed kinematics theory were used to find the accurate sensors'output transform parameters. The work described provides a novel method for ground-truth database construction. This approach has the advantages that it does not require any special outer device and can be implemented automatically, while still produce high fidelity performance.