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Eric Whitmire

Researcher at University of Washington

Publications -  34
Citations -  853

Eric Whitmire is an academic researcher from University of Washington. The author has contributed to research in topics: Wearable computer & Haptic technology. The author has an hindex of 12, co-authored 31 publications receiving 546 citations. Previous affiliations of Eric Whitmire include Facebook & Nvidia.

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Proceedings ArticleDOI

Haptic Revolver: Touch, Shear, Texture, and Shape Rendering on a Reconfigurable Virtual Reality Controller

TL;DR: Haptic Revolver is a handheld virtual reality controller that renders fingertip haptics when interacting with virtual surfaces through an actuated wheel that raises and lowers underneath the finger to render contact with a virtual surface.
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Sound Localization Sensors for Search and Rescue Biobots

TL;DR: This work presents a vision-based automated system for an objective assessment of biobotic navigation capability on Madagascar hissing cockroaches and reports the most precise control results obtained with insect biobots so far both manually and autonomously.
Journal ArticleDOI

DigiTouch: Reconfigurable Thumb-to-Finger Input and Text Entry on Head-mounted Displays

TL;DR: DigiTouch is presented, a reconfigurable glove-based input device that enables thumb-to-finger touch interaction by sensing continuous touch position and pressure and improves the reliability of continuous touch tracking and estimating pressure on resistive fabric interfaces.
Proceedings ArticleDOI

HyperCam: hyperspectral imaging for ubiquitous computing applications

TL;DR: HyperCam provides a low-cost implementation of a multispectral camera and a software approach that automatically analyzes the scene and provides a user with an optimal set of images that try to capture the salient information of the scene.
Journal ArticleDOI

AuraRing: Precise Electromagnetic Finger Tracking

TL;DR: This work presents AuraRing, a wearable magnetic tracking system designed for tracking fine-grained finger movement that requires no runtime supervised training, ensuring user and session independence and develops two different approaches to pose reconstruction.