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

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Abstract

We describe a quasi-determinstic 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 of 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 (head, hands, knees, etc.) 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 degrees in angle), to the comparable precision of the camera / Lidar systems, and to the fine temporal and spatial synchronization between the systems, we can reliably associate the multipaths to the keypoints. This enables modeling the backscattering properties of the individual keypoints, properties 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 accuracy without compromising the computational efficiency of raytracing. Finally, the channel realizations are compared to the measurements, substantiating the model's high fidelity.

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Keywords

Wireless (RF), Virtual / augmented reality, Protocol design and standardization, Next generation networks, Networking, Network test and measurement, Network security and robustness, Network modeling and analysis, Modeling and simulation research, Mobile and wireless networking, Mobile, Machine learning, Location based services, Internet of Things (IoT), Image and signal processing, Hardware for AI, Electromagnetics, Biometrics, Artificial intelligence, Applied AI, AI measurement and evaluation and Advanced communications

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