Simulation for Noncontact Radar-Based Physiological Sensing Using Depth-Camera-Derived Human 3D Model with Electromagnetic Scattering Analysis

TL;DR

This paper introduces a realistic simulation method for radar-based physiological sensing that uses depth camera data to model human body geometry and motion, improving the accuracy of respiratory monitoring simulations.

Contribution

The study presents a novel simulation approach utilizing depth-camera-derived human models for more accurate radar signal prediction in physiological sensing.

Findings

Achieved 7.5% improvement in radar image correlation

Achieved 58.2% improvement in displacement correlation

Achieved 3.2% improvement in spectrogram correlation

Abstract

This study proposes a method for simulating signals received by frequency-modulated continuous-wave radar during respiratory monitoring, using human body geometry and displacement data acquired via a depth camera. Unlike previous studies that rely on simplified models of body geometry or displacement, the proposed approach models high-frequency scattering centers based on realistic depth-camera-measured body shapes and motions. Experiments were conducted with six participants under varying conditions, including varying target distances, seating orientations, and radar types, with simultaneous acquisition from the radar and depth camera. Relative to conventional model-based methods, the proposed technique achieved improvements of 7.5%, 58.2%, and 3.2% in the correlation coefficients of radar images, displacements, and spectrograms, respectively. This work contributes to the generation of radar-based physiological datasets through simulation and enhances our understanding of factors affecting the accuracy of non-contact sensing.