Doppler radiance fields, built by factorizing Wi-Fi Doppler projections into 3D velocities and resampling them on a fixed sphere grid, improve cross-user activity recognition accuracy from 51.5% to 54.8% on the UTHAMO dataset.
A tutorial-cum- survey on self-supervised learning for wi-fi sensing: Trends, challenges, and outlook,
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DoRF: Doppler Radiance Fields for Robust Human Activity Recognition Using Wi-Fi
Doppler radiance fields, built by factorizing Wi-Fi Doppler projections into 3D velocities and resampling them on a fixed sphere grid, improve cross-user activity recognition accuracy from 51.5% to 54.8% on the UTHAMO dataset.