  Heart rate and respiratory rate measurement is a vital step for diagnosing
many diseases. Non-contact camera based physiological measurement is more
accessible and convenient in Telehealth nowadays than contact instruments such
as fingertip oximeters since non-contact methods reduce risk of infection.
However, remote physiological signal measurement is challenging due to
environment illumination variations, head motion, facial expression, etc. It's
also desirable to have a unified network which could estimate both heart rate
and respiratory rate to reduce system complexity and latency. We propose a
convolutional neural network which leverages spatial attention and channel
attention, which we call it dual attention network (DAN) to jointly estimate
heart rate and respiratory rate with camera video as input. Extensive
experiments demonstrate that our proposed system significantly improves heart
rate and respiratory rate measurement accuracy.
