Practical Human Sensing in the Light

Tianxing Li, Qiang Liu, and Xia Zhou
Department of Computer Science, Dartmouth College


We present StarLight, an infrastructure-based sensing system that reuses light emitted from ceiling LED panels to reconstruct fine-grained user skeleton postures continuously in real time. It relies on only a few (e.g., 20) photodiodes placed at optimized locations to passively capture low-level visual clues (light blockage information), with neither cameras capturing sensitive images, nor on-body devices, nor electromagnetic interference. It then aggregates the blockage information of a large number of light rays from LED panels and identifies best-fit 3D skeleton postures. StarLight greatly advances the prior light-based sensing design by dramatically reducing the number of intrusive sensors, overcoming furniture blockage, and supporting user mobility. We build and deploy StarLight in a 3.6 m x 4.8 m office room, with customized 20 LED panels and 20 photodiodes. Experiments show that StarLight achieves 13.6° mean angular error for five body joints and reconstructs a mobile skeleton at a high frame rate (40 FPS). StarLight enables a new unobtrusive sensing paradigm to augment today‚Äôs mobile sensing for continuous and accurate behavioral monitoring.


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We sincerely thank our shepherd Marco Gruteser and the reviewers for their insightful comments. We thank Ronald Peterson for his helpful suggestions on fabricating PCB boards. We also thank Zhao Tian, Chuankai An, Weichen Wang and Linfeng Wang for their support on our study. Special thanks to George Hito '18 and Yifei Xie '16 for the help on the demo video. This work is supported in part by the National Science Foundation under grants CNS-1421528 and CNS- 1552924. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the funding agencies or others.


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