Shuffle-rPPGNet: Efficient Network With Global Context for Remote Heart Rate Variability Measurement

Abstract

In the field of remote physiological indicator measurement, there are relatively few studies on measuring heart rate variability (HRV). Moreover, some methods using deep neural networks are computationally intensive due to high network complexity. In this paper, a HRV measurement method is proposed based on an efficient three dimensional convolutional neural network (3DCNN) model defined as Shuffle-rPPGNet. The basic idea of the proposed method is to extract more precise remote photoplethysmography (rPPG) signals from facial videos by this model and then calculate relevant indicators of HRV through the post-processed rPPG signals. Shuffle-rPPGNet uses a lightweight network structure named ShuffleNetV2 as a baseline to construct an efficient 3DCNN with additional parts including 3D Global Context, 3D Channel Shuffle and Up-sample blocks, etc. The proposed method has been evaluated on two public datasets, i.e., UBFC-rPPG and PURE datasets. The results show that the proposed method has better performance in HRV measurement compared to the state-of-the-art methods. Especially, the mean absolute errors of the average values of the normal-to-normal interval sequence reach 2.35 and 6.62 respectively on these two public datasets.