Task performance with wrist input utilizing surface detection in AR

Abstract

This thesis delves into the realm of wrist-based input utilizing surface detection within Augmented Reality (AR) environments, with a primary focus on smartwatches. The central aim is to conduct a comprehensive assessment of the usability and potential of this emerging input method.

The research objectives encompass a multifaceted approach, including the quantitative assessment of performance, exploration of qualitative user experiences, investigation of user adaptation dynamics between different interaction methods, and systematic analysis of user feedback. These goals culminate in addressing the overarching question: "Can smartwatches utilizing surface detection serve as the next generation of AR controllers?"

To achieve these objectives, immersive demos were designed to evaluate wrist-based input in real-world AR scenarios. A quantitative evaluation was performed through an immersive keyboard demo, providing data-driven insights into the technology's performance. Simultaneously, qualitative exploration unfolded during user testing sessions with a music sequencer demo, shedding light on the intricacies of human interaction with AR interfaces.

The research results suggest promise for wrist-based input, particularly for non-expert users who found it more effective than hand-tracking. However, challenges persist, and further refinement, including improved smartwatch accuracy through advanced machine learning models, is essential for its full realization as AR controllers. This research contributes valuable insights to the evolving landscape of AR input methods, informing future developments that have the potential to shape the future of AR interactions.