We explore the use of multimodal input to predict the landing position of a ray pointer while selecting targets in a virtual reality (VR) environment. We first extend a prior 2D Kinematic Template Matching technique to include head movements. This new technique, Head-Coupled Kinematic Template Matching, was found to improve upon the existing 2D approach, with an angular error of 10.0° when a user was 40% of the way through their movement. We then investigate two additional models that incorporated eye gaze, which were both found to further improve the predicted landing positions. The first model, Gaze-Coupled Kinematic Template Matching resulted in angular error of 6.8° for reciprocal target layouts and 9.1° for random target layouts, when a user was 40% of the way through their movement. The second model, Hybrid Kinematic Template Matching, resulted in angular error of 5.2° for reciprocal target layouts and 7.2° for random target layouts when a user was 40% of the way through their movement. We also found that using just the current gaze location resulted in sufficient predictions in many conditions. We reflect on our results by discussing the broader implications of utilizing multimodal input to inform selection predictions in VR.

Raycasting is a common method for target selection in virtual reality (VR). However, it results in selection ambiguity whenever a ray intersects multiple targets that are located at different depths. To resolve these ambiguities, we estimate object depth by projecting the closest intersection between the gaze and controller rays onto the controller ray. An evaluation of this method found that it significantly outperformed a previous eye convergence depth estimation technique. Based on these results, we developed GazeRayCursor, a novel selection technique that enhances Raycasting, by leveraging gaze for object depth estimation. In a second study, we compared two variations of GazeRayCursor with RayCursor, a recent technique developed for a similar purpose, in a dense target environment. The results indicated that GazeRayCursor decreased selection time by 45.0% and reduced manual depth adjustments by a factor of 10 in a dense target environment. Our findings showed that GazeRayCursor is an effective method for target disambiguation in VR selection without incurring extra effort.

This work presents PITAS, a thin-sheet robotic material composed of a reversible phase transition actuating layer and a heating/sensing layer. The synthetic sheet material enables non-expert makers to create shape-changing devices that can locally or remotely convey physical information such as shape, color, texture and temperature changes. PITAS sheets can be manipulated into various 2D shapes or 3D geometries using subtractive fabrication methods such as laser, vinyl, or manual cutting or an optional additive 3D printing method for creating 3D objects. After describing the design of PITAS, this paper also describes a study conducted with thirteen makers to gauge the accessibility, design space, and limitations encountered when PITAS is used as a soft robotic material while designing physical information communication devices. Lastly, this work reports on the results of a mechanical and electrical evaluation of PITAS and presents application examples to demonstrate its utility.

Gaze-based interaction is a fast and ergonomic type of hands-free interaction that is often used with augmented and virtual reality when pointing at targets. Such interaction, however, can be cumbersome whenever user, tracking, or environmental factors cause eye tracking errors. Recent research has suggested that fallback modalities could be leveraged to ensure stable interaction irrespective of the current level of eye tracking error. This work thus presents Weighted Pointer interaction, a collection of error-aware pointing techniques that determine whether pointing should be performed by gaze, a fallback modality, or a combination of the two, depending on the level of eye tracking error that is present. These techniques enable users to accurately point at targets when eye tracking is accurate and inaccurate. A virtual reality target selection study demonstrated that Weighted Pointer techniques were more performant and preferred over techniques that required the use of manual modality switching.

This work explores the design of marking menus for gaze-based AR/VR menu selection by expert and novice users. It first identifies and explains the challenges inherent in ocular motor control and current eye tracking hardware, including overshooting, incorrect selections, and false activations. Through three empirical studies, we optimized and validated design parameters to mitigate these errors while reducing completion time, task load, and eye fatigue. Based on the findings from these studies, we derived a set of design guidelines to support gaze-based marking menus in AR/VR. To overcome the overshoot errors found with eye-based expert marking menu behaviour, we developed StickyPie, a marking menu technique that enables scale-independent marking input by estimating saccade landing positions. An evaluation of StickyPie revealed that StickyPie was easier to learn than the traditional technique (i.e., RegularPie) and was 10% more efficient after 3 sessions.