Deriving Motion Constraints in Finger Joints of Individualized Hand Model for Manipulation by Data Glove

In this paper, we propose a novel method of skeleton estimation for the purpose of constructing and manipulating individualized hand models via a data glove. To reconstruct actual hand accurately, we derive motion constraints in fully 6-DOF at the joints without assuming either a center of rotation or a joint axis. The constraints are derived by regression analysis on the configuration of the finger segments with respect to the sensor data. In order to acquire pairs of the configuration and the sensor data, we introduce graspable reference objects for reproducing postures with and without the data glove. To achieve accurate regression on fewer samples, we introduce a regression model according to a detailed investigation using a fused motion capture system, which enabled us to perform optical motion capture and sensor data collection simultaneously. The effectiveness of the method is demonstrated through practical applications involving grasped reference objects.