Reconstructing Hand Poses Using Visible Light

Abstract

Free-hand gestural input is essential for emerging user interactions. We present Aili, a table lamp reconstructing a 3D hand skeleton in real time, requiring neither cameras nor on-body sensing devices. Aili consists of an LED panel in a lampshade and a few low-cost photodiodes embedded in the lamp base. To reconstruct a hand skeleton, Aili combines 2D binary blockage maps from vantage points of different photodiodes, which describe whether a hand blocks light rays from individual LEDs to all photodiodes. Empowering a table lamp with sensing capability, Aili can be seamlessly integrated into the existing environment. Relying on such low-level cues, Aili entails lightweight computation and is inherently privacy-preserving. We build and evaluate an Aili prototype. Results show that Aili’s algorithm reconstructs a hand pose within 7.2 ms on average, with 10.2° mean angular deviation and 2.5-mm mean translation deviation in comparison to Leap Motion. We also conduct user studies to examine the privacy issues of Leap Motion and solicit feedback on Aili’s privacy protection. We conclude by demonstrating various interaction applications Aili enables.

Publication
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies(IMWUT), vol. 1, no. 3, 2017.
Xia Zhou
Xia Zhou
Associate Professor

My research interests include distributed robotics, mobile computing and programmable matter.

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