Passive Respiration Detection via mmWave Communication Signal Under Interference

TL;DR

This paper presents a mmWave-based passive respiration detection system that accurately identifies human respiration even amidst significant human motion interference, using neural networks and an empirical model for rapid rate counting.

Contribution

It introduces a novel mmWave sensing system operating at 60.48 GHz capable of detecting respiration under interference, with a neural network and empirical model for improved accuracy and speed.

Findings

Over 90% detection accuracy under interference

Effective respiratory rate counting within 10 seconds

Neural network enhances detection robustness

Abstract

Recent research has highlighted the detection of human respiration rate using commodity WiFi devices. Nevertheless, these devices encounter challenges in accurately discerning human respiration amidst the prevailing human motion interference encountered in daily life. To tackle this predicament, this paper introduces a passive sensing and communication system designed specifically for respiration detection in the presence of robust human motion interference. Operating within the 60.48 GHz band, the proposed system aims to detect human respiration even when confronted with substantial human motion interference within close proximity. Subsequently, a neural network is trained using the collected data by us to enable human respiration detection. The experimental results demonstrate a consistently high accuracy rate over 90\% of the human respiration detection under interference, given an adequate sensing duration. Finally, an empirical model is derived analytically to achieve the respiratory rate counting in 10 seconds.