Quasi-stationary Slice Detection-Based Robust Respiration Rate Estimation under Large-scale Random Body Movement

TL;DR

This paper introduces a novel two-stage radar-based method for accurate respiration rate estimation that effectively handles large-scale body movements by detecting quasi-stationary signal slices using an enhanced deep neural network.

Contribution

It proposes a new two-stage scheme utilizing deep learning and quasi-stationary slice detection to improve respiration rate estimation under complex movement scenarios.

Findings

Accurately detects quasi-stationary slices in micro-Doppler spectra.

Reduces respiration rate estimation errors during large-scale body movements.

Demonstrates superior performance over existing methods in experimental tests.

Abstract

Radar-based non-contact respiration rate (RR) measurement has become increasingly popular due to its convenience, non-intrusiveness, and low cost. However, it is still quite challenging to accurately acquire vital signs estimation in complex measurement scenarios with large-scale random body movements (RBM), particularly for RR estimation due to strong low-frequency interferences. To cope with the RBM challenge in RR estimation, we propose a novel two-stage RR estimation scheme involving detecting the portion of signals, called as quasi-stationary slices, exhibiting the quasi-stationary pattern. At the detection stage, an enhanced deep neural network framework incorporating the dynamic snake convolution is exploited to detect the quasi-stationary slices in the micro-Doppler spectra. At the estimation stage, we mitigate RBM interferences and achieve accurate RR estimation by only using the portion of ridges consistent with the location of detected quasi-stationary slice. Extensive experimental results demonstrate that our proposed scheme can accurately detect quasi-stationary slices under normal scenarios with large-scale RBM, thereby reducing the error of subsequent RR estimation.