TR2020-158
Human Pose and Seat Occupancy Classification with Commercial MMWave WiFi
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- , "Human Pose and Seat Occupancy Classification with Commercial MMWave WiFi", IEEE Global Communications Conference (GLOBECOM), DOI: 10.1109/GCWkshps50303.2020.9367535, December 2020.BibTeX TR2020-158 PDF
- @inproceedings{Yu2020dec,
- author = {Yu, Jianyuan and Wang, Pu and Koike-Akino, Toshiaki and Wang, Ye and Orlik, Philip V.},
- title = {{Human Pose and Seat Occupancy Classification with Commercial MMWave WiFi}},
- booktitle = {IEEE Global Communications Conference (GLOBECOM)},
- year = 2020,
- month = dec,
- publisher = {IEEE},
- doi = {10.1109/GCWkshps50303.2020.9367535},
- isbn = {978-1-7281-7307-8},
- url = {https://www.merl.com/publications/TR2020-158}
- }
- , "Human Pose and Seat Occupancy Classification with Commercial MMWave WiFi", IEEE Global Communications Conference (GLOBECOM), DOI: 10.1109/GCWkshps50303.2020.9367535, December 2020.
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MERL Contacts:
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Research Areas:
Communications, Computational Sensing, Machine Learning, Signal Processing
Abstract:
Our previous studies introduced a mid-grained intermediate-level channel measurement — spatial beam signalto-noise ratios (SNRs) that are inherently available and defined in the 60-GHz IEEE 802.11ad/ay standards — for the fingerprinting-based indoor localization. In this paper, we take one step further to use the mid-grained channel measurement for human monitoring applications including human pose and seat occupancy classifications. The effectiveness of the mid-grained channel measurement is validated by an in-house experimental dataset that includes 5 separate data collection sessions using classical classification methods and modern deep neural networks. Our preliminary result shows that mmWave beam SNRs are capable of delivering high classification accuracy above 90%.
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