Evaluating Visual-Spatiotemporal Coregistration of a Physics-based Virtual Reality Haptic Interface

Abstract

This study aimed to evaluate the visual-spatiotemporal coregistration of the real and virtual objects’ movement dynamics in a low-cost physics-based virtual reality (VR) system that provides real cutaneous and kinesthetic haptic feedback of the objects instead of computer-generated haptic feedback. Twelve healthy participants performed three human-robot collaborative (HRC) sequential pick-and-place lifting tasks while both motion capture and VR systems respectively traced the movement kinematics of the real and virtual objects. We used an iterative closest point algorithm to transform the 3D spatial point clouds of the VR system into the motion capture system. We employed a novel algorithm and principal component analysis to respectively calculate visual and spatiotemporal coregistration precisions between virtual and real objects. Results showed a high correlation (r > 0.96) between real and virtual objects’ movement dynamics and linear and angular coregistration errors of less than 5 cm and 8°, respectively. The trend also revealed a low temporal registration error of <12 ms and was only found along the vertical axis. The visual registration data indicated that the real cutaneous and kinesthetic haptics provided by the physical objects in the virtual environment enhanced proprioception and visuomotor functions of the users.

GeoBotsVR: A Robotics Learning Game for Beginners with Hands-on Learning Simulation

“…In terms of mode of learning, researchers suggest that virtual reality (VR) enhances learning by increasing engagement and immersion [4,8,[18][19][20]30]. VR's spatial navigation reduces the cognitive load in programming learning, surpassing traditional text-based methods [10].…”