Human grasping interaction capture and analysis

Abstract

We design a system to capture, clean, and segment a high quality database of hand based grasping and manipulation. We capture interactions with a large collection of everyday objects. Optical marker-based motion capture and glove data are combined in a physics-based filter to improve the quality of thumb motion. Sensors stitched into our glove provide recordings of the pressure image across the fingers and the palm. We evaluate different segmentation techniques for processing motion and pressure data. Finally, we describe examples that explain how the data will be useful in applications such as virtual reality and the design of physics-based control of virtual and robotic hands.