A New Paradigm for Trusted Respiratory Monitoring Via Consumer Electronics-grade Radar Signals

TL;DR

This work tackles privacy leakage from USI in consumer-grade mmWave radar for respiratory monitoring by introducing Tru-RM, a privacy-preserving paradigm combining Attribute Feature Decoupling (AFD), Flexible Perturbation Encryptor (FPE), and Robust Perturbation Tolerable Network (PTN). AFD separates universal respiratory features from personal-difference USI, while FPE applies key-controlled amplitude and phase perturbations to anonymize USI without harming respiration signals. PTN, built upon Spectral Distribution Alignment (SDAB) and Temporal Mixer Block (TMB), reconstructs robust respiratory features from perturbed signals, ensuring accurate monitoring. Extensive experiments demonstrate strong anonymization of USI (IRAC dropping from 83.38% to 32.62%) with only minor degradation in respiratory MAE (≈1.2 bpm) and low variability, across distances, breathing patterns, and durations, highlighting practical privacy-enabled sensing for collaborative health applications.

Abstract

Respiratory monitoring is an extremely important task in modern medical services. Due to its significant advantages, e.g., non-contact, radar-based respiratory monitoring has attracted widespread attention from both academia and industry. Unfortunately, though it can achieve high monitoring accuracy, consumer electronics-grade radar data inevitably contains User-sensitive Identity Information (USI), which may be maliciously used and further lead to privacy leakage. To track these challenges, by variational mode decomposition (VMD) and adversarial loss-based encryption, we propose a novel Trusted Respiratory Monitoring paradigm, Tru-RM, to perform automated respiratory monitoring through radio signals while effectively anonymizing USI. The key enablers of Tru-RM are Attribute Feature Decoupling (AFD), Flexible Perturbation Encryptor (FPE), and robust Perturbation Tolerable Network (PTN) used for attribute decomposition, identity encryption, and perturbed respiratory monitoring, respectively. Specifically, AFD is designed to decompose the raw radar signals into the universal respiratory component, the personal difference component, and other unrelated components. Then, by using large noise to drown out the other unrelated components, and the phase noise algorithm with a learning intensity parameter to eliminate USI in the personal difference component, FPE is designed to achieve complete user identity information encryption without affecting respiratory features. Finally, by designing the transferred generalized domain-independent network, PTN is employed to accurately detect respiration when waveforms change significantly. Extensive experiments based on various detection distances, respiratory patterns, and durations demonstrate the superior performance of Tru-RM on strong anonymity of USI, and high detection accuracy of perturbed respiratory waveforms.

Figures (10)

• Figure 1: Identity recognition based on mmWave radar.
• Figure 2: Key processing steps in Tru-RM.
• Figure 3: Comparison of respiratory time domain waveforms of different users.
• Figure 4: The structure of FPE for perturbation encryption.
• Figure 5: Robust respiratory feature extraction model PTN for perturbed signals