Thesis Defence: Thumb-To-Index-Finger Interactions for Dual Task Scenarios

Omang Ravikant Baheti, supervised by Dr. Mohammad Khalad Hasan, will defend their thesis titled “Thumb-To-Index-Finger Interactions for Dual Task Scenarios” in partial fulfillment of the requirements for the degree of Master of Science in Computer Science.

An abstract for Omang Ravikant Baheti’s thesis is included below.

Defences are open to all members of the campus community as well as the general public. Please email khalad.hasan@ubc.ca to receive the Zoom link for this defence.

Abstract

Head-mounted displays (HMDs) and emerging augmented reality (AR) interfaces promise ubiquitous, in-situ computing. However, current interaction techniques remain limited in situations where users must divide attention between multiple activities, such as walking or driving. This thesis investigates thumb-to-index (T2I) finger interactions as a compact, one-handed input modality that enables interaction in dual-task scenarios, specifically in a walking and driving context.

This work introduces One Finger Warrior, a T2I interaction technique that segments the thumb-index contact area to support up to 36 distinct actions, enabling dense yet ergonomic interaction for mobile and wearable AR systems. First, this thesis systematically characterizes the performance (e.g., speed, accuracy, and comfort) of T2I gestures under different mobility conditions. Results show that mobility does not significantly affect gesture performance. Building on this finding, we design a T2I-based text entry method and examine its performance envelope using two layout configurations: an optimized layout and a sub-optimal layout. Participants achieve average text-entry speeds of 12.05 words per minute (WPM) with the optimized layout and 8.31 WPM with the sub-optimal layout, with minimal performance degradation under mobility.

Next, we investigate the suitability of T2I interactions for in-vehicle non-driving-related tasks using AR displays. We evaluate the full gesture set in a simulated driving scenario, comparing interaction performance when the hand is positioned on versus off the steering wheel. Results indicate similar gesture performance in both conditions. We then compare T2I interaction using two information mapping techniques: direct mapping and indirect mapping against a touchscreen during a menu selection task while driving. Results show that T2I interaction enables faster task completion than touchscreen input.

Overall, this thesis demonstrates that T2I interaction provides a reliable, ergonomic input modality for mobile and in-vehicle AR systems. The findings advance the design of hand-proximate user interfaces and establish T2I interaction as a practical approach for compact, context-sensitive input in everyday AR scenarios.