Optimizing Video-Based Respiration Monitoring: Motion Artifact Reduction and Adaptive ROI Selection

In non-contact respiratory monitoring, reducing motion artifact and selecting the appropriate Region of Interest (ROI) pose significant challenges. Most motion artifact removal methods rely on signal periodicity assumptions, while respiratory signals usually are non-periodic in real-world scenarios. Existing automated ROI selection approaches are mostly primarily impacted by the texture of clothing, absence of chest landmarks, and obstruction of face. To improve the quality of respiratory signals, in this study, we propose a framework for automatic respiratory ROI selection based on video, namely, Optimizing Video-based Respiration Monitoring (OVRM), which consists of peak-trough adaptive motion artifact removal and characteristic-driven adaptive ROI selection. This motion artifact removal strategy removes motion artifacts by using a dynamic ratio-based judgment mechanism, and reconstructs signals using sinusoidal interpolation. The adaptive ROI method scores signals based on periodicity, similarity, smoothness, and energy, selecting the highest-scoring blocks as the ROIs to match respiratory signals efficiently. Experimental results, validated across four datasets, demonstrate that OVRM effectively reduces signal noise caused by subject movement and outperforms state-of-the-art non-contact respiratory monitoring algorithms. The dataset and code are publicly available at: https://github.com/zxx5058/OVRM.