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.Clinical relevance- Our contactless respiratory monitoring can utilize a consumer RGB camera to offer a significant benefit in continuous monitoring of neonatal monitoring, sleep monitoring, telemedicine or telehealth, home fitness with mild physical movement, and emotion detection in the clinic and remote locations.

Enhanced Contactless Heart Rate Monitoring Using Camera with Motion Artifact Removal During Physical Activities.

Contactless monitoring of heart rate (HR) can improve passive and continuous tracking of cardiovascular activities and overall people's health. Remote photoplethysmography (rPPG) using a camera eliminates the need for a wearable device. rPPG-based HR has shown promising results to be accurate and comparable to conventional methods such as contact PPG. Most experiments use stationary subjects while motion is known to affect the accuracy of remote PPG. In this paper, a novel methodology is introduced to enhance the accuracy and reliability of HR monitoring based on rPPG in the presence of physical activities like Yoga. This method quickly and accurately tracks HR and analyzes head motion to exclude unreliable data within short windows of rPPG signals. The method was tested with smartphone video data collected from 60 subjects when they are doing activities with varying levels of movement. Results show that our method without motion removal improves the accuracy of the HR readings by 0.7 bpm, reaching 3.57 bpm on average for a 30-sec-window. The accuracy is further improved by another 1.3 bpm after removing the motion artifacts, and reaches 2.29 bpm.Clinical relevance- The enhancement of HR readings from shorter rPPG signal with motion tolerance during physical activities can ultimately help with a more reliable HR tracking of people in uncontrolled settings like home which is a critical step towards remote health-care or wellness tracking.

Estimating SpO2 with Deep Oxygen Desaturations from Facial Video Under Various Lighting Conditions: A Feasibility Study.

This paper presents a feasibility study to collect data, process signals, and validate accuracy of peripheral oxygen saturation (SpO2) estimation from facial video in various lighting conditions. We collected facial videos using RGB camera, without auto-tuning, from subjects when they were breathing through a mouth tube with their nose clipped. The videos were record under four lighting conditions: warm color temperature and normal brightness, neutral color temperature and normal brightness, cool color temperature and normal brightness, neutral color temperature and dim brightness. The air inhaled by the subjects was manually controlled to gradually induce hypoxemia and lower subjects' SpO2 to as low as 81%. We first extracted the remote photoplethysmogram (rPPG) signals from the videos. We applied the principle of pulse oximetry and extracted the ratio of ratios (RoR) for two color combinations: Red/Blue and Red/Green. Next, we assessed SpO2 estimation accuracy against the ground truth, a Transfer Standard Pulse Oximeter. We have achieved an RMSE of 1.93% and a PCC of 0.97 under the warm color temperature and normal brightness lighting condition using leave-one-subject-out cross validation between two subjects. The results have demonstrated the feasibility to estimate SpO2 remotely and accurately using consumer level RGB camera with suitable camera configuration and lighting condition.Clinical Relevance- This work demonstrates that SpO2 can be estimated accurately using an RGB camera without auto-tuning and under warm color temperature, enabling continuous SpO2 monitoring applications that require noncontact sensing.

Motion- Based Respiratory Rate Estimation with Motion Artifact Removal Using Video of Face and Upper Body.

Respiratory rate (RR) is a significant indicator of health conditions. Remote contactless measurement of RR is gaining popularity with recent respiratory tract infection awareness. Among various methods of contactless RR measurement, a video of an individual can be used to obtain an instantaneous RR. In this paper, we introduce an RR estimation based on the subtle motion of the head or upper chest captured on an RGB camera. Motion-based respiratory monitoring allows us to acquire RR from individuals with partial face coverings, such as glasses or a face mask. However, motion-based RR estimation is vulnerable to the subject's voluntary movement. In this work, adaptive selection between face and chest regions plus a motion artifact removal technique enables us to obtain a much cleaner respiratory signal from the video recordings. The average mean absolute error (MAE) for controlled and natural breathing is 1.95 BPM using head motion only and 1.28 BPM using chest motion only. Our results demonstrate the possibility of continuous monitoring of breathing rate in real-time with any personal device equipped with an RGB camera, such as a laptop or a smartphone.