Evaluation of Touch Gesture Performance on Surfaces in Virtual Reality
The recent development of Virtual Reality (VR) technologies allows people to do their professional work as well as get some entertainment in VR environments. Users typically interact with VR environments by using VR controllers or in-air gestures. While these input methods allow 6-DOF manipulation of virtual objects, precise manipulation in VR is still challenging. VR controllers do not utilize dexterous manipulations that human fingers are capable of, and in-air gestures do not provide any haptic feedback when fingers touch virtual objects. To enable a more precise and effective VR manipulation, researchers have proposed mid-air haptic feedback techniques that produce the feeling of touching an object at the fingertip and showed promising results for selection tasks. However, it is not studied how well these methods would work for more complex manipulation tasks such as drawing that require precise and continuous control of a finger movement.
This thesis aims to understand the requirement for ensuring precise manipulation in VR by investigating the effect of different levels of haptic feedback on the performance of manipulations tasks in VR. Three levels of haptic feedback were implemented for the study: 1) no haptic feedback, which the user relies only on the visual feedback, 2) virtual haptic feedback, which the user can feel the haptic feedback at the fingertip during contact, and 3) physical haptic feedback, which the user performs touch interactions on a physical surface. In the user study, error, task completion time and performance quality were measured for selection, tracking, and drawing tasks. Although the main user study could not be conducted due to the COVID-19 situation, a preliminary study showed that having a physical surface is crucial in enabling accurate and precise manipulations in VR and also reducing physical and mental task load of completing manipulation tasks.
- Prof. Gregory J. Gerling (Chair)
- Prof. Seongkook Heo (Advisor)
- Prof. Yuan Tian
- Prof. Sara Riggs