Zooming and panning are fundamental input actions for exploring complex 2D and 3D scenes and data such as images, maps, and designs. Multi-touch zoom/pan interactions have been proven effective on mobile devices, and have been directly ported to HMDs, where they are typically accomplished by analogous but relatively large-scale movements of both hands. We argue that such motions are inefficient and induce fatigue and explore how the eye-tracking features of HMDs can be leveraged to achieve improvements. We evaluated three interaction techniques that combine gaze with two-handed, one-handed, and head-based input in a study (N = 24) that contrasts them against a baseline two-handed technique. The results indicate that gaze-assisted two- and one-handed techniques outperform the baseline (17%-36% faster), while our head-based technique achieves similar performance to the Baseline but leaves the hands free for other tasks. We further developed a VR application demonstrating these techniques and validating their practical applicability.

Eye tracking offers new opportunities for interaction and data collection as users adopt head-mounted displays (HMDs). However, most HMDs either lack a built-in eye tracker or restrict access to gaze data, preventing researchers from realizing their full potential. Third-party eye trackers are a potential solution; however, existing methods for integrating them into HMDs are rarely evaluated and heavily depend on the specific software platform or device. Therefore, we present a validated open-source framework for cross-device eye tracking. We developed the framework on Pupil Labs Neon and assessed performance across three representative HMDs—the Apple Vision Pro, Meta Quest 3, and XREAL Air 2 Ultra. Results showed our framework significantly outperformed Pupil Lab’s implementation on the Quest 3 while maintaining consistent accuracy across other devices. By releasing the framework, this work establishes a validated foundation for cross-device gaze tracking, enabling accessible and reproducible eye tracking research in augmented and virtual reality environments.

Wireless earbuds are an appealing platform for wearable computing on-the-go. However, their small size and out-of-view location mean they support limited different inputs. We propose finger identification input on earbuds as a novel technique to resolve these problems. This technique involves associating touches by different fingers with different responses. To enable it on earbuds, we adapted prior work on smartwatches to develop a wireless earbud featuring a magnetometer that detects fields from a magnetic ring. A first study reveals participants achieve rapid, precise earbud touches with different fingers, even while mobile (time: 0.98s, errors: 5.6%). Furthermore, touching fingers can be accurately classified (96.9%). A second study shows strong performance with a more expressive technique involving multi-finger double-taps (inter-touch time: 0.39s, errors: 2.8%) while maintaining high accuracy (94.7%). We close by exploring and evaluating the design of earbud finger identification applications and demonstrating the feasibility of our system on low-resource devices.

Wearable Augmented Reality headsets are inherently mobile: they enable hands-free and immersive interaction while on the go.Despite this, research into input methods that cater to mobility issues, such as the instabilities introduced by canonical tasks such as walking, remains in its infancy.This paper addresses this omission by presenting StabilizAR, a technique to enhance head cursor input while walking.It introduces a novel cursor velocity limit activated by the mutual alignment of head and eye vectors that enhances fine-grained targeting without compromising input speed during large-scale cursor motion.It integrates this with a target scoring system that reduces the precision required during selection by accruing proximity-based estimates of a user's intended target.Two studies show these combined techniques dramatically increase targeting performance-boosting success rates from 6% to 91% while mobile-and elevate measures of usability and user preference.They show StabilizAR's potential to enable genuinely mobile HMD use.