A non-contact SpO2 estimation using video magnification and infrared data

Peripheral oxygen saturation (SpO2) is one important vital sign to be monitored in individuals, whose health is fragile, such as the elderly. Contactless SpO2 monitoring using RGB cameras has been already developed with satisfactory results. This work explores the case of achieving an acceptable level of performance, when the lightning conditions are not optimal, particularly during night time, by processing solely infrared low-cost camera recordings. The Eulerian Video Magnification (EVM) technique was used to enhance the subtle differences in skin pixel intensity in the facial area. Two approaches were explored for performing regression: one using 12 novel features extracted from the amplified photoplethysmography (PPG) signal and Generalized Additive Models and a second using a 3D Convolution Neural Network (CNN) architecture on the raw amplified forehead video. The root mean square error in the estimated SpO2 levels using both methods is minimal and in the accepted range for these applications.