Target selection is a fundamental task in interactive Augmented Reality (AR) systems. Predicting the intended target of selection in such systems can provide users with a smooth, low-friction interaction experience. Our work aims to predict gaze-based target selection in AR headsets with eye and head endpoint distributions, which describe the probability distribution of eye and head 3D orientation when a user triggers a selection input. We first conducted a user study to collect users’ eye and head behavior in a gaze-based pointing selection task with two confirmation mechanisms (air tap and blinking). Based on the study results, we then built two models: a unimodal model using only eye endpoints and a multimodal model using both eye and head endpoints. Results from a second user study showed that the pointing accuracy is improved by approximately 32% after integrating our models into gaze-based selection techniques.

Region selection is a fundamental task in interactive systems. In 2D user interfaces, users typically use a rectangle selection tool to formulate a region using a mouse or touchpad. Region selection in 3D spaces, especially in Augmented Reality (AR) Head-Mounted Displays (HMDs) is different and challenging because users need to select an intended region via freehand mid-air gestures or eye-based actions that are touchless interactions. In this work, we aim to fill in the gap in the design of region selection techniques in AR HMDs. We first analyzed and discretized the interaction procedure of region selection and explored design possibilities for each step. We then developed four techniques for region selection in AR HMDs, which leveraged users' hand and gaze for unimodal or multimodal interaction. The techniques were evaluated via a user study with a controlled region selection task. The findings led to three design recommendations and two proof-of-concept application examples.

Target selection in dense virtual reality (VR) environments is challenging. Prior work has explored different controller-based raycasting techniques to assist target selection in such environments. However, limited research has focused on selection via mid-air barehand, which represents another major input metaphor for immersive environments. In this paper, we first review the existing raycasting selection techniques for dense VR environments. Based on this, we propose and develop two freehand pointing selection techniques—HandDepthCursor and HandConeGrid, and implement MultiFingerBubble, a recently-proposed technique. We then conduct a user study to compare and evaluate their performance and experience in a target selection task in dense VR environments. Our results suggest that HandDepthCursor and HandConeGrid led to significantly faster and more accurate selection performance, and lower perceived workload and arm fatigue. In addition, HandConeGrid showed a distinct advantage in high-density environments.

Alphanumeric and special characters are essential during text entry. Text entry in virtual reality (VR) is usually performed on a virtual Qwerty keyboard to minimize the need to learn new layouts. As such, entering capitals, symbols, and numbers in VR is often a direct migration from a physical/touchscreen Qwerty keyboard—that is, using the mode-switching keys to switch between different types of characters and symbols. However, there are inherent differences between a keyboard in VR and a physical/touchscreen keyboard, and as such, a direct adaptation of mode-switching via switch keys may not be suitable for VR. The high flexibility afforded by VR opens up more possibilities for entering alphanumeric and special characters using the Qwerty layout. In this work, we designed two controller-based raycasting text entry methods for alphanumeric and special characters input ( Layer-ButtonSwitch and Key-ButtonSwitch ) and compared them with two other methods ( Standard Qwerty Keyboard and Layer-PointSwitch ) that were derived from physical and soft Qwerty keyboards. We explored the performance and user preference of these four methods via two user studies (one short-term and one prolonged use), where participants were instructed to input text containing alphanumeric and special characters. Our results show that Layer-ButtonSwitch led to the highest statistically significant performance, followed by Key-ButtonSwitch and Standard Qwerty Keyboard , while Layer-PointSwitch had the slowest speed. With continuous practice, participants’ performance using Key-ButtonSwitch reached that of Layer-ButtonSwitch . Further, the results show that the key-level layout used in Key-ButtonSwitch led users to parallel mode switching and character input operations because this layout showed all characters on one layer. We distill three recommendations from the results that can help guide the design of text entry techniques for alphanumeric and special characters in VR.

Object selection is essential in virtual reality (VR) head-mounted displays (HMDs). Prior work mainly focuses on enhancing and evaluating techniques for selecting a single object in VR, leaving a gap in the techniques for multi-object selection, a more complex but common selection scenario. To enable multi-object selection, the interaction technique should support group selection in addition to the default pointing selection mode for acquiring a single target. This composite interaction could be particularly challenging when using freehand gestural input. In this work, we present an empirical comparison of six freehand techniques, which are comprised of three mode-switching gestures (Finger Segment, Multi-Finger, and Wrist Orientation) and two group selection techniques (Cone-casting Selection and Crossing Selection) derived from prior work. Our results demonstrate the performance, user experience, and preference of each technique. The findings derive three design implications that can guide the design of freehand techniques for multi-object selection in VR HMDs.

In recent years, Virtual Reality (VR) Head-Mounted Displays (HMD) have been used to provide an immersive, first-person view in real-time for the remote-control of Unmanned Ground Vehicles (UGV). One critical issue is that it is challenging to perceive the distance of obstacles surrounding the vehicle from 2D views in the HMD, which deteriorates the control of UGV. Conventional distance indicators used in HMD take up screen space which leads clutter on the display and can further reduce situation awareness of the physical environment. To address this issue, we proposed off-screen, in-device feedback using vibro-tactile and/or light-visual cues to provide real-time distance information for the remote control of UGV. Results from a study show a significantly better performance with either feedback type, reduced workload and improved usability in a driving task that requires continuous perception of the distance between the UGV and its environmental objects or obstacles. Our findings show a solid case for in-device vibro-tactile and/or light-visual feedback to support remote manipulation of UGV that highly relies on distance perception of objects.