An Early Warning System Based on Visual Feedback for Light-Based Hand Tracking Failures in VR Head-Mounted Displays

Abstract

State-of-the-art Virtual Reality (VR) Head-Mounted Displays (HMDs) enable users to interact with virtual objects using their hands via built-in camera systems. However, the accuracy of the hand movement detection algorithm is often affected by limitations in both camera hardware and software, including image processing & machine learning algorithms used for hand skeleton detection. In this work, we investigated a visual feedback mechanism to create an early warning system that detects hand skeleton recognition failures in VR HMDs and warns users in advance. We conducted two user studies to evaluate the system's effectiveness. The first study involved a cup stacking task, where participants stacked virtual cups. In the second study, participants performed a ball sorting task, picking and placing colored balls into corresponding baskets. During both of the studies, we monitored the built-in hand tracking confidence of the VR HMD system and provided visual feedback to the user to warn them when the tracking confidence is 'low'. The results showed that warning users before the hand tracking algorithm fails improved the system's usability while reducing frustration. The impact of our results extends beyond VR HMDs, any system that uses hand tracking, such as robotics, can benefit from this approach.