Date of Award

3-2023

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Systems Engineering and Management

First Advisor

Michael E. Miller, PhD

Abstract

The human machine interface in 5th generation aircraft has not evolved proportionally with advances in display size and data density. The traditional cursor slew method fails to rapidly relocate the cursor, especially on large displays. Previous studies at the Air Force Test Pilot School and Air Force Institute of Technology identified methods that have the potential to improve the human machine interface. This research expanded upon those studies by providing an assessment of head tracking technology as a secondary method of cursor manipulation. Specifically, this study examined the effects that visual feedback (visible and invisible head tracking cursors) and cursor configurations (Z-Axis and X/Y-Axis snap button) had on performance in a target selection task. A Fitts Law regression was conducted to fit the collected data to a predictive model, but this was unsuccessful. Dependent variables such as time to initiate head tracking snap, accuracy of head tracking snap, and total time to select target were examined to compare the different configurations. After initial data analysis was complete, an assessment of learning effects was conducted. The initial data analysis found all head tracking configurations to be faster than the traditional cursor slew method. Visible conditions were consistently more accurate and had lower total selection times than the invisible conditions. Invisible conditions had faster times to initiate the cursor snap, indicating that the participants were not attempting to make fine adjustments with the head tracking cursor. There were no observed learning effects in this study. The resulting conclusions are discussed and recommendations for future research are proposed including study of fatigue in the target selection task, target selection as a secondary task, and the combination of rhino pointing with eye tracking capabilities.

AFIT Designator

AFIT-ENV-MS-23-M-187

Comments

A 12-month embargo was observed.

Approved for public release: 88ABW-2023-0257

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