  With the increasing use of smartphones in our daily lives, these devices have
become capable of performing many complex tasks. Concerning the need for
continuous monitoring of vital signs, especially for the elderly or those with
certain types of diseases, the development of algorithms that can estimate
vital signs using smartphones has attracted researchers worldwide. In
particular, researchers have been exploring ways to estimate vital signs, such
as heart rate, oxygen saturation levels, and respiratory rate, using algorithms
that can be run on smartphones. However, many of these algorithms require
multiple pre-processing steps that might introduce some implementation
overheads or require the design of a couple of hand-crafted stages to obtain an
optimal result. To address this issue, this research proposes a novel
end-to-end solution to mobile-based vital sign estimation using deep learning
that eliminates the need for pre-processing. By using a fully convolutional
architecture, the proposed model has much fewer parameters and less
computational complexity compared to the architectures that use fully-connected
layers as the prediction heads. This also reduces the risk of overfitting.
Additionally, a public dataset for vital sign estimation, which includes 62
videos collected from 35 men and 27 women, is provided. Overall, the proposed
end-to-end approach promises significantly improved efficiency and performance
for on-device health monitoring on readily available consumer electronics.
