RF-Vital: Radio-Based Contactless Respiration Monitoring for a Moving Individual

The noncontact respiration rate measurement (nRRM) method allows a system to monitor the breathing patterns of an individual without physical contact, which is crucial for regular health monitoring. Current nRRM approaches primarily depend on detecting minor variations in RGB profiles reflected from a camera to remotely extract respiration signals. However, these methods require continuous pixel-level tracking, which restricts their use on individuals in quasi-stationary sitting positions. To address this limitation, we propose a radiofrequency (RF)-Vital model, which leverages RF signals to extend the applicability of nRRM methods to individuals who exhibit global motions (GMs) and even walk around. The core idea of the RF-Vital model lies in the unique characteristics of RF signals: the RF signals received from a moving individual capture both their respiratory motions (RMs) and GMs through linear superposition while simultaneously providing the reflections of GM alone. To fully utilize such unique properties, we introduce a new RF modality that allows stable inclusion of micro-level respiration signatures, even when GMs are present. Additionally, we optimize the RF-Vital model using a novel multitask adversarial learning framework combined with a new loss function, which facilitates the direct mapping of the desired RMs as well as the self-supervised removal of GMs, thereby effectively filtering out RMs from mixtures of GMs and RMs. The proposed RFvital model was evaluated using newly published data sets. It demonstrated state-of-the-art performance in static conditions and achieved the significant milestone of enabling nRRM under moving conditions.