Measuring Oxygen Saturation With Smartphone Cameras Using Convolutional Neural Networks

Arterial oxygen saturation (SaO(2)) is an indicator of how much oxygen is carried by hemoglobin in the blood. Having enough oxygen is vital for the functioning of cells in the human body. Measurement of SaO(2) is typically estimated with a pulse oximeter, but recent works have investigated how smartphone cameras can be used to infer SaO(2). In this paper, we propose methods for the measurement of SaO(2) with a smartphone using convolutional neural networks and preprocessing steps to better guard against motion artifacts. To evaluate this methodology, we conducted a breath-holding study involving 39 participants. We compare the results using two different mobile phones. We compare our model with the ratio-of-ratios model that is widely used in pulse oximeter applications, showing that our system has significantly lower mean absolute error (2.02%) than a medical pulse oximeter.