Expiratory Flow and Volume Estimation Through Thermal-CO$ {}_{\text{2}} $ Imaging

Objective: In this work, we introduce a quantitative non-contact respiratory evaluation method for fine-grain exhale flow and volume estimation through Thermal-$CO_{2}$ imaging. This provides a form of respiratory analysis that is driven by visual analytics of exhale behaviors, creating quantitative metrics for exhale flow and volume modeled as open-air turbulent flows. This approach introduces a novel form of effort-independent pulmonary evaluation enabling behavioral analysis of natural exhale behaviors. Methods: $CO_{2}$ filtered infrared visualizations of exhale behaviors are used to obtain breathing rate, volumetric flow estimations (L/s) and per-exhale volume (L) estimations. We conduct experiments validating visual flow analysis to formulate two behavioral Long-Short-Term-Memory (LSTM) estimation models generated from visualized exhale flows targeting per-subject and cross-subject training datasets. Results: Experimental model data generated for training on our per-individual recurrent estimation model provide an overall flow correlation estimate correlation of $R^{2}=0.912$ and volume in-the-wild accuracy of 75.65–94.44%. Our cross-patient model extends generality to unseen exhale behaviors, obtaining an overall correlation of $R^{2}=0.804$ and in-the-wild volume accuracy of 62.32–94.22%. Conclusion: This method provides non-contact flow and volume estimation through filtered $CO_{2}$ imaging, enabling effort-independent analysis of natural breathing behaviors. Significance: Effort-independent evaluation of exhale flow and volume broadens capabilities in pulmonological assessment and long-term non-contact respiratory analysis.