Dual Attention Network for Heart Rate and Respiratory Rate Estimation

TL;DR

This paper introduces a dual attention neural network that accurately estimates heart rate and respiratory rate from video, addressing challenges like environmental variations and motion, to enhance non-contact physiological monitoring.

Contribution

The paper presents a novel dual attention network that jointly estimates heart and respiratory rates from video, improving accuracy over existing methods.

Findings

Significant accuracy improvements demonstrated in experiments

Effective handling of environmental and motion challenges

Unified model reduces system complexity and latency

Abstract

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.