AmplitudeArrow: On-the-Go AR Menu Selection Using Consecutive Simple Head Gestures and Amplitude Visualization.

Area of Science:

• Human-Computer Interaction
• Augmented Reality
• Wearable Computing

Background:

• Heads-up computing requires unobtrusive input methods for on-the-go tasks.
• Current modalities like voice and gestures have limitations for seamless interaction.
• Head gestures present an underexplored modality for heads-up computing.

Purpose of the Study:

• To introduce and evaluate the AmplitudeArrow (AA) technique for augmented reality (AR) menu selection.
• To demonstrate the efficacy of simple, consecutive head gestures as an input modality for heads-up computing.
• To assess AA's performance and robustness in real-world, on-the-go scenarios.

Main Methods:

• Developed the AmplitudeArrow (AA) technique utilizing head yaw and pitch gestures for AR menu selection.
• Designed a selection mechanism where head pitch amplitude controls icon selection confirmation.
• Conducted user studies to evaluate AA's accuracy, speed, and robustness during walking.

Main Results:

• AA achieved high accuracy with a low error rate (5%) and fast selection times (1.5s per selection).
• The technique demonstrated robustness against walking-induced head perturbations and environmental factors.
• Optimal performance was observed with up to six icon columns (twelve icons) in a defined visual angle.

Conclusions:

• Consecutive simple head gestures, as implemented in AA, are an effective input modality for heads-up computing.
• AA offers a viable solution for on-the-go AR menu selection, enhancing user interaction.
• The AmplitudeArrow technique shows promise for future heads-up computing applications requiring intuitive control.