Real-Time Respiration Monitoring via Motion Artifact Suppression and Quality-Guided Peak Detection

Real-time respiration monitoring faces several challenges including network latency in remote settings, limited computational resources, and increased motion artifacts. Although many existing non-contact respiration algorithms are designed for offline processing and thus overlook these limitations, real-time applications demand greater robustness, efficiency, and adaptability to dynamic conditions. In this study, a lightweight framework called Quality-Guided Respiration Monitoring (QGRM) is proposed. This framework integrates a two-stage motion artifact suppression module and a quality-guided peak detection (QGPD) module. The former enhances signal stability through FIR filtering and amplitude limiting, while the latter improves the estimation of the respiration rate by filtering false peaks based on amplitude and zero-crossing constraints. The experimental results obtained with both the public OVRM dataset and a self-constructed simulated dataset demonstrate that QGRM achieves superior accuracy and robustness compared to state-of-the-art methods.