Dual Attention Network for Heart Rate and Respiratory Rate Estimation
This work provides a unified, non-contact method for vital sign monitoring in telehealth, reducing infection risk and system complexity, though it appears incremental as it builds on existing attention mechanisms.
The authors tackled the problem of non-contact heart rate and respiratory rate estimation from camera videos, addressing challenges like illumination variations and motion, and proposed a dual attention network that significantly improves measurement accuracy.
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.