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Robert J. Teather

Publications and source records attributed to Robert J. Teather.

3 recordsLinked to original sources

Comparing Hand and Controller Avatars with Hand Tracking and Controller-Based Interaction

Previous research suggests that the congruency between common VR input devices - such as controllers or hand tracking - and their visual representations (e.g., hand or controller avatars) influences user experience and performance. However, the specific effects of input-avatar combinations remain underexplored. We study the effects of common input devices (hand tracking and controllers) and visual representations (hand and controller avatars) on performance and perceived success in target acquisition tasks. We included both grasping and pinching gestures across 16 combinations of input, avatar, and target size. Results indicate that hand tracking benefits from any form of visual representation - even when mismatched - achieving up to 5.8% greater accuracy compared to having no avatar, likely due to its reliance on visual feedback in the absence of a physical prop. Controllers were generally preferred and offered faster task completion. However, mismatched avatars had a stronger negative effect with controllers, particularly when the virtual gesture did not align with the physical action, leading to a 5.6% drop in accuracy compared to the matched condition - suggesting that inaccurate feedback can be more disruptive than having no avatar feedback at all.

cs.HC

Comparing Controller-Free Pointing Techniques Across Depth for 2D Selection in Augmented Reality

This paper presents a systematic evaluation of five controller-free pointing techniques for 2D target selection in AR, using ISO 9241-411. We compared them across multiple depths (2 m, 6 m, 10 m) in terms of movement time, accuracy, throughput, and workload (NASA TLX). Head- and eye-based pointing significantly outperformed the hand-based methods (Finger, Wrist, and Arm); Head input was the most accurate and remained the most consistent across depth. Depth significantly impacted performance, with complex interactions with target size and distance. Our results offer a comprehensive empirical basis for selecting appropriate controller-free techniques in depth-varying AR tasks.

cs.HC

Adaptic: A Shape Changing Prop with Haptic Retargeting

We present Adaptic, a novel "hybrid" active/passive haptic device that can change shape to act as a proxy for a range of virtual objects in VR. We use Adaptic with haptic retargeting to redirect the user's hand to provide haptic feedback for several virtual objects in arm's reach using only a single prop. To evaluate the effectiveness of Adaptic with haptic retargeting, we conducted a within-subjects experiment employing a docking task to compare Adaptic to non-matching proxy objects (i.e., Styrofoam balls) and matching shape props. In our study, Adaptic sat on a desk in front of the user and changed shapes between grasps, to provide matching tactile feedback for various virtual objects placed in different virtual locations. Results indicate that the illusion was convincing: users felt they were manipulating several virtual objects in different virtual locations with a single Adaptic device. Docking performance (completion time and accuracy) with Adaptic was comparable to props without haptic retargeting.

cs.HC