RPPG Signal Measurement Based on Time-do-main Attention Network

Abstract

The measurement of heart rate without touching the object being measured is referred to as “non-contact heart rate estimation.” Telemedicine, education, and a variety of other fields can all benefit from this approach, which is capable of achieving continuous heart rate measurement under certain conditions. The video segmentation network and the backbone network are cascaded in this paper’s cascaded heart rate estimation model. Adding temporal attention based on the existing 3D convolutional network model, the video segmentation network divides the video into multiple segments and fuses the relevant temporal features. It then sends the fusion results to the backbone network with the temporal attention mechanism. Mechanisms and modules like spatiotemporal convolution to make heart rate measurement more reliable and use a custom joint loss function to cut out other kinds of periodic signals’ influence. The trial results show the way that the proposed calculation can successfully lessen movement ancient rarity and light change clamor.Additionally, the dataset UBFC-rPPG demonstrates the model’s efficacy.