Assessment of physiological states from contactless face video: a sparse representation approach

The vital signs are estimated from remote photoplethysmography (rPPG) using the sparse representation signal reconstruction approach. The rPPG signal is used to estimate the physical parameters with the help of a non-invasive smartphone camera. This paper presents a health monitoring method by estimating vital signs using an RGB video camera and uses a pre-specified dictionary based on a hybrid discrete ridgelet transform with a Ricker wavelet basis function, to reconstruct a sparse signal prone to less error. The physical parameters such as heart rate (HR), breathing rate (BR), heart rate variability (HRV), and SpO2 are estimated using a smartphone video camera with the proposed sparse signal reconstruction technique. The inter-beat intervals (IBIs) are used to extract the power ratio in the frequency domain. Changes in HRV are more discriminative indicators of cognitive stress than those in HR and BR. The physiological states such as stress and fatigue could be measured using IBIs ratio in the frequency domain. The morning and evening dataset sessions are recruited for this experiment to check the stress and fatigue factors based on the power ratio extracted from the IBI signal. In the results, a lower mean absolute probability error value shows that the proposed method produces better results than state-of-the-art methods.

Automated summary

The paper presents a health monitoring method using remote photoplethysmography (rPPG) and a smartphone camera to estimate vital signs. By reconstructing a sparse signal, the method accurately estimates physical parameters such as heart rate, breathing rate, heart rate variability, and SpO2. The power ratio in the frequency domain of inter-beat intervals is used to measure physiological states like stress and fatigue.