Hand visualization significantly impacts user performance in virtual reality (VR), particularly in tasks requiring precise finger movements. Common hand avatar visualizations, such as opaque or transparent, often occlude critical elements or distract users, potentially reducing accuracy. To address this issue, we investigated adaptive hand visibility techniques, which vary the hand avatar visibility based on the current movement sub-task aiming to enhance user performance. We evaluated these techniques on a VR-based Pedicle Screw Placement task with 15 participants. Each participant inserted virtual screws into a spine using five hand visualization conditions: opaque, transparent, invisible, speed-based visibility (hand visibility changes with hand speed), and position-based visibility (hand visibility adapts to the proximity of critical elements). Results showed that speed-based and position-based visualizations significantly improved accuracy, usability, and overall performance compared to traditional methods. The widely adopted opaque visualization yielded the lowest accuracy and usability. Our findings emphasize the benefits of adaptive hand visualization based on sub-tasks, recommending its implementation in VR applications to increase usability and user accuracy.

The virtual hand interaction technique is one of the most common interaction techniques used in virtual reality (VR) systems. A VR application can be designed with different hand visualization styles, which might impact motor dexterity. In this paper, we aim to investigate the effects of three different hand visualization styles — transparent, opaque, and invisible — on participants’ performance through a VR-based Purdue Pegboard Test (PPT). A total of 24 participants were recruited and instructed to place pegs on the board as quickly and accurately as possible. The results indicated that using the invisible hand visualization significantly increased the number of task repetitions completed compared to the opaque hand visualization. However, no significant difference was observed in participants’ preference for the hand visualization styles. These findings suggest that an invisible hand visualization may enhance performance in the VR-based PPT, potentially indicating the advantages of a less obstructive hand visualization style. We hope our results can guide developers, researchers, and practitioners when designing novel virtual hand interaction techniques.