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

Published

July 9, 2025

Author(s)

Jack Chuang, Raied Caromi, Jelena Senic, Samuel Berweger, Neeraj Varshney, Jian Wang, Anuraag Bodi, Camillo Gentile, Nada Golmie

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.

Citation

IEEE Transactions on Wireless Communications

Pub Type

Journals

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Keywords

6G, JCAS, camera, Lidar, joint communications and sensing, 28~GHz, phased-array antennas

Citation

Chuang, J. , Caromi, R. , Senic, J. , Berweger, S. , Varshney, N. , Wang, J. , Bodi, A. , Gentile, C. and Golmie, N. (2025), Quasi-Deterministic Channel Propagation Model for Human Sensing: Gesture Recognition Use Case, IEEE Transactions on Wireless Communications, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956405 (Accessed July 12, 2025)