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.
Data The UTHAMO dataset, introduced in [18], is utilized to assess the performance of the proposed method
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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.