Quasi-Deterministic Channel Propagation Model for Human Sensing: Gesture Recognition Use Case

We describe a quasi-deterministic channel propagation model for human gesture recognition reduced from real-time measurements with our context-aware channel sounder, considering four human subjects and 20 distinct body motions, for a total of 120 000 channel acquisitions. The sounder features a radio-frequency (RF) system with 28 GHz phased-array antennas to extract discrete multipaths backscattered from the body in path gain, delay, azimuth angle-of-arrival, and elevation angle-of-arrival domains, and features camera / Lidar systems to extract discrete keypoints that correspond to salient parts of the body in the same domains as the multipaths. Thanks to the precision of the RF system, with average error of only 0.1 ns in delay and 0.2∘ in angle, we can reliably associate the multipaths to the keypoints. This enables modeling the backscatter properties of individual body parts, such as Radar cross-section and correlation time. Once the model is reduced from the measurements, the channel is realized through raytracing a stickman of keypoints – the deterministic component of the model to represent generalizable motion – superimposed with a Sum-of-Sinusoids process – the stochastic component of the model to render enhanced accuracy. Finally, the channel realizations are compared to the measurements, substantiating the model's high fidelity.