Head, Gaze, or Finger? Comparing Object Selection Techniques in Augmented Reality for People with Low Vision

2026-07-07

AI Summary

This study addresses the lack of systematic accessibility research in augmented reality (AR) object selection techniques for people with low vision, particularly those with central visual field loss. Employing a mixed-methods approach, the authors compare the performance and user experience of three interaction modalities—head pointing, gaze, and finger pointing—in both seated and mobile scenarios. The experiments involve real-world target selection tasks using head tracking, eye tracking, and finger pointing combined with gaze confirmation. Results reveal that gaze-based selection enables the fastest initial targeting in static settings, while head pointing offers the highest stability and lowest cognitive load. Notably, participants with central visual field loss significantly preferred finger pointing and reported a stronger sense of control, providing empirical evidence and design guidance for accessible AR interaction tailored to low-vision users.

Abstract

Augmented reality (AR) can enhance visual perception for people with low vision (PLV) by overlaying multimodal information. Selection-based augmentation further allows users to flexibly choose and augment relevant information while reducing distraction and visual clutter. However, little is known about the ability and preferences of PLV in performing object selection techniques in AR, considering their potential visual and gaze control challenges. To understand what selection techniques are suitable for PLV to support selection-based AR augmentations, we conducted a mixed-methods study with 20 PLV and 18 sighted controls who performed target selection tasks using three input techniques -- head, gaze, and finger pointing with dwell-based confirmation -- in two real-world scenarios (sitting vs. on the go). We found that for PLV, gaze-based selection enabled the fastest initial pointing when sitting and comparable overall selection time to head-based selection in both scenarios; however, due to reduced gaze stability, head-based selection remained the most stable and the least mentally demanding. Uniquely, participants with central vision loss preferred finger-based selection, reporting a greater sense of control. Our results provide empirical insights into accessible AR interaction techniques and selection-based vision enhancements for PLV.

Problem

• low vision
• augmented reality
• object selection
• accessibility
• human-computer interaction

Innovation

• augmented reality
• low vision
• object selection
• accessible interaction
• gaze control