A Vision Based Method for Real-Time Respiration Rate Estimation Using a Recursive Fourier Analysis

In this paper, we propose a simple yet effective, computer vision based method for estimating respiration rate in real-time from the thoraco-abdominal video of a subject being monitored. The periodic motion of the chest wall of the subject is captured through the optical flow in the video sequence. The frequency of the chest wall motion is estimated by performing a Fourier analysis on the time sequence of the optical flow vectors. We present how to perform the Fourier analysis recursively leveraging the sequential nature of a video to speed-up our method. Unlike other methods, our method does not require a selection of region of interest because our method aggregates out-of-phase optical flows by factoring out the their relative phase differences. Unlike many existing methods, our method does not require to be reinitialized if the subject changes the posture during the observation. Our method works with different postures and different views (frontal, side, etc.) of the subject. Our method is very simple to implement. We evaluate our method against an impedance pneumograph and demonstrate the high accuracy of our method on thoracoabdominal videos of many subjects wearing a wide variety of clothing.