Measurement of Blood Oxygen based on Remote-Photoplethysmography

SpO2, also known as blood oxygen saturation, is a vital physiological indicator in clinical care. Since the outbreak of COVID-19, silent hypoxia has been one of the most serious symptoms. This symptom makes the patient's SpO2 drop to an extremely low level without discomfort and causes medical care delay for many patients. Therefore, regularly checking our SpO2 has become a very important matter. Recent work has been looking for convenient and contact-free ways to measure SpO2 with cameras. However, most previous studies were not robust enough and didn't evaluate their algorithms on the data with a wide SpO2 range. In this paper, we proposed a novel non-contact method to measure SpO2 by using the weighted K-nearest neighbors (KNN) algorithm. Five features extracted from the RGB traces, POS, and CHROM signals were used in the KNN model. Two datasets using different ways to lower the SpO2 were constructed for evaluating the performance. The first one was collected through the breath-holding experiment, which induces more motion noise and confuses the actual blood oxygen features. The second dataset was collected at Song Syue Lodge, which locates at an elevation of 3150 meters and has lower oxygen concentration in the atmosphere making the SpO2 drop between the range of 80% to 90% without the need of holding breath. The proposed method outperforms the benchmark algorithms on the leave-one-subject-out and cross-dataset validation.