Human Pose and Seat Occupancy Classification with Commercial MMWave WiFi


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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