Contactless Monitoring of Respiratory Rate and Breathing Absence from Head Movements Using an RGB Camera

Contactless vital sign monitoring is more demanding for long-term, continuous, and unobtrusive measurements. Camera-based respiratory monitoring is receiving growing interest with advanced video technologies and computational power. The volume variations of the lungs for airflow changes create a periodic movement of the torso, but identifying the torso is more challenging than face detection in a video. In this paper, we present a unique approach to monitoring respiratory rate (RR) and breathing absence by leveraging head movements alone from an RGB video because respiratory motion also influences the head. Besides our novel RR estimation, an independent algorithm for breathing absence detection using signal feature extraction and machine learning techniques identifies an apnea event and improves overall RR estimation accuracy. The proposed approach was evaluated using videos from 30 healthy subjects who performed various breathing tasks. The breathing absence detector had 0.87 F1 score, 0.9 sensitivity, and 0.85 specificity. The accuracy of spontaneous breathing rate estimation increased from 2.46 to 1.91 bpm MAE and 3.54 to 2.7 bpm RMSE when combining the breathing absence result with the estimated RR.