Real-Time Visual Respiration Rate Estimation with Dynamic Scene Adaptation

摘要

In this paper, we present a vision based method for respiration rate estimation which can automatically adapt to the scene changes. We capture a video of the subjects thoraco-abdominal region and compute optical flow field at each video frame. The optical flow field changes periodically with the periodic chest wall motion. The pattern of the chest wall motion is captured through the estimation of a principal flow field. The principal flow field is automatically updated with time to cope with the scene changes. Thus, our method can adapt itself to the changes of the posture of a subject. Besides, in our method we do not need to select any region of interest unlike other methods. Yet our method is computationally very inexpensive and simple to implement. We test our method on many human volunteers with a wide variety of their clothing. We compare our method against the gold standard method of impedance pneumography and have found a very high accuracy.