Hands-free interaction is essential when users’ hands are occupied with primary tasks. A key challenge is unifying precise, UI-independent selection and continuous dragging without explicit mode switching. This paper introduces two novel techniques: Aligner (spatial eye-head alignment) and Nodder (gestural decomposition of a nod); and evaluates them against an established Winker technique, a gestural clutch using single-eye closure. A user study with controlled Fitts’ law and dragging tasks, alongside a practical application, evaluated the techniques. Results show that Aligner offers superior selection precision but creates a visuomotor conflict during dragging; Nodder optimizes movement efficiency for large-amplitude targets despite higher physical effort; and Winker provides the fastest performance but is susceptible to accidental activation. These findings inform the design of future hands-free systems by highlighting the context-dependent nature of this interaction challenge.

Figure 1: The On-body Icons interface enables users to quickly launch apps in AR and offers onboarding by letting them experience the feature firsthand (a-c) and guiding them through the setup of on-body shortcuts (d, f).It also supports methods for setting up shortcuts beyond "grab-and-place" (e), such as "select-and-point" (g).

Spatial interaction relies on fast and accurate visual acquisition. In this work, we analyse how visual acquisition and tracking of targets presented in a head-mounted display is affected by the user moving linearly at walking and jogging paces. We study four reference frames in which targets can be presented: Head and World where targets are affixed relative to the head and environment, respectively; HeadDelay where targets are presented in the head coordinate system but follow head movement with a delay, and novel Path where targets remain at fixed distance in front of the user, in the direction of their movement. Results of our study in virtual reality demonstrate that the more stable the target is relative to the environment, the faster and more precise it can be fixated. The results have practical significance as head-mounted displays enable interaction during mobility, and in particular when eye tracking is considered as input.

Eye tracking filters have been shown to improve accuracy of gaze estimation and input for stationary settings. However, their effectiveness during physical movement remains underexplored. In this work, we compare common online filters in the context of physical locomotion in extended reality and propose alterations to improve them for on-the-go settings. We conducted a computational experiment where we simulate performance of the online filters using data on participants attending visual targets located in world-, path-, and two head-based reference frames while standing, walking, and jogging. Our results provide insights into the filters' effectiveness and factors that affect it, such as the amount of noise caused by locomotion and differences in compensatory eye movements, and demonstrate that filters with saccade detection prove most useful for on-the-go settings. We discuss the implications of our findings and conclude with guidance on gaze data filtering for interaction in extended reality.

Gaze-Hand Alignment has recently been proposed for multimodal selection in 3D. The technique takes advantage of gaze for target pre-selection, as it naturally precedes manual input. Selection is then completed when manual input aligns with gaze on the target, without need for an additional click method. In this work we evaluate two alignment techniques, Gaze&Finger and Gaze&Handray, combining gaze with image plane pointing versus raycasting, in comparison with hands-only baselines and Gaze&Pinch as established multimodal technique. We used Fitts’ Law study design with targets presented at different depths in the visual scene, to assess effect of parallax on performance. The alignment techniques outperformed their respective hands-only baselines. Gaze&Finger is efficient when targets are close to the image plane but less performant with increasing target depth due to parallax.