Abstract

This paper addresses the process of respiration, which involves providing oxygen to the body’s cells, blood, and tissues. The significance of respiratory signals as predictors of diverse physical conditions, including respiratory health and disorders, is emphasized. Technologies such as pressure-sensor belts for chest or abdominal movement detection, carbon-dioxide sensors placed under the nose, and photoplethysmography (PPG) have been developed for real-time monitoring of respiratory signals. However, wearable devices with direct body contact may inconvenience sleeping patients, newborns, or individuals with sensitive skin. Noncontact and noninvasive respiratory rate measurements utilizing facial videos have been explored, albeit within controlled environments. This study introduces a novel approach to measure robust respiratory signals and facial respiratory rates (FRR) using facial videos. The proposed method employs bounding box stabilization to eliminate high-frequency components stemming from face detection. Real-time coordinate correction is implemented to ensure accurate respiration signal calculation across varying environments. The method defines a region of interest (ROI) on the face, computes RGB data, and transforms the RGB channel into a YCgCo channel. The respiratory signal is subsequently derived by applying partial zero padding-based fast Fourier transform (FFT) and inverse FFT (iFFT) to the Cg signal within the frequency band associated with respiration. The FRR is accurately measured by analyzing peak-to-peak intervals within the respiratory signal. The efficacy of the proposed method is demonstrated through a comparative analysis of respiratory rates calculated from PPG devices and facial videos across four scenarios (indoor, outdoor, car, and drone). The results highlight the reliability and precision of the suggested approach in accurately computing respiratory rates across diverse environments.

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