Assessment of ROI Selection for Facial Video-Based rPPG

In general, facial image-based remote photoplethysmography (rPPG) methods use color-based and patch-based region-of-interest (ROI) selection methods to estimate the blood volume pulse (BVP) and beats per minute (BPM). Anatomically, the thickness of the skin is not uniform in all areas of the face, so the same diffuse reflection information cannot be obtained in each area. In recent years, various studies have presented experimental results for their ROIs but did not provide a valid rationale for the proposed regions. In this paper, to see the effect of skin thickness on the accuracy of the rPPG algorithm, we conducted an experiment on 39 anatomically divided facial regions. Experiments were performed with seven algorithms (CHROM, GREEN, ICA, PBV, POS, SSR, and LGI) using the UBFC-rPPG and LGI-PPGI datasets considering 29 selected regions and two adjusted regions out of 39 anatomically classified regions. We proposed a BVP similarity evaluation metric to find a region with high accuracy. We conducted additional experiments on the TOP-5 regions and BOT-5 regions and presented the validity of the proposed ROIs. The TOP-5 regions showed relatively high accuracy compared to the previous algorithm’s ROI, suggesting that the anatomical characteristics of the ROI should be considered when developing a facial image-based rPPG algorithm.

Pulse Rate Variability Analysis Using Remote Photoplethysmography Signals

Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is an indicator of the health status of an individual’s autonomic nervous system. A representative method for measuring BVP is contact PPG (CPPG). CPPG may cause discomfort to a user, because the sensor is attached to the finger for measurements. In contrast, noncontact remote PPG (RPPG) extracts BVP signals from face data using a camera without the need for a sensor. However, because the existing RPPG is a technology that extracts a single pulse rate rather than a continuous BVP signal, it is difficult to extract additional health status indicators. Therefore, in this study, PRV analysis is performed using lab-based RPPG technology that can yield continuous BVP signals. In addition, we intended to confirm that the analysis of PRV via RPPG can be performed with the same quality as analysis via CPPG. The experimental results confirmed that the temporal and frequency parameters of PRV extracted from RPPG and CPPG were similar. In terms of correlation, the PRVs of RPPG and CPPG yielded correlation coefficients between 0.98 and 1.0.

Automated, high-accuracy, remote measurement of heart rate

Arrhythmia is a marked symptom of many cardiovascular diseases. The accurate and in time detection of heart rate can greatly reduce its harm to people. However, it is still a challenge to automatedly and remotely measure the heart rate in daily life, because the environment factor of the measurement changes variously, such as the changing light intensity, the movement of people, and the uncertain distance from sensor to people. In this study, we accurately measured the heart rate of people at the distance of 4.8 meters under different intensity of light just by using a surveillance camera. After a short color video (20 sec) of a person's hand was captured by this camera, a method based on Fast Fourier Transform Algorithm (FFT) is proposed to extract the blood volume pulse wave to calculate the heart rate. By comparing the real heart rate with the results measured by electrocardiography (ECG), the accuracy of heart rate measurement using the method proposed in this study is 98.65% within 4.0 meters, and the accuracy can reach 90% within 5.6 meters. Our experiments also demonstrated that this method can accurately obtain the heart rate even when the intensity of light is below 32 LUX ( office environment 300-500 LUX). The strong environmental suitability makes this method can be applied to many occasions, such as community clinic, old peoples' home, classroom, and other public space.

325 Stress-Induced Physiological Profiles Among Different Phenotype Insomniacs

Introduction Stress reactivity and autonomic nervous system (ANS) dysregulation have been suggested to be the pathophysiology of insomnia. Based on the finding PSG-measured short sleep duration was associated with higher morbidity of metabolic and cardiovascular disease. Vgontzas and Fernandez-Mandoza (2013) proposed that objective sleep duration is a biomarker for insomnia phenotypes. The phenotype with short objective sleep duration is associated with increased stress-related physiological hyperarousal. The present study aims to test this hypothesis by comparing the stress-induced cardiovascular reactivity between insomnia patients with short and long objective sleep durations. Methods 27 insomnia patients (age mean 34.48 ±12.87, Male: Female= 6:21) without comorbidity of psychiatric, medical or sleep disorders participated in this study. They went through one night of 8-hour PSG recording and were divided into two groups by their total sleep time with a cutoff of 6 hours. Nine participants were in short sleep duration group and 18 in longer sleep duration group. Psychophysiological reactivity profile, as recorded with EKG, skin conductance (SC), body temperature (BT), blood volume pulse (BVP), respiration rate (RR), was measured under three conditions: baseline resting state, arithmetic word problems solving, and recovery resting state. Results Both groups showed similar stress physiological response with increased heart rate (HR) and SC, nearly equivalent BT and BVP, and decreased RR when solving arithmetic problems, and opposite reaction during recovery resting state. Mann-Whitney U test comparing the changes from baseline resting state on all the psychophysiological measures between two phenotypes of insomnia showed no significant differences: stress-induced heart-rate (U=106, p=.119.) recovery heart-rate (U=44, p=.095), stress-induced skin conductance (U=104.5, p=.132),recovery skin conductance (U=51.5, p=.198), stress-induced body temperature (U=79, p=.897),recovery body temperature (U=60.5, p=.418), stress-induced blood volume pulse amplitude (U=77, p=1.0), and recovery blood volume pulse amplitude (U=69, p=.735), stress-induced respiration rate (U=76, p =.696), and recovery respiration rate (U=85, p=.658). Conclusion Our results indicate that the insomnia phenotypes with short and long objective sleep duration are not different in their stress-induced physiological responses. Future studies are needed to confirm these results and to explore other mechanisms for the increased metabolic and cardiovascular disease risk in insomnia patients with short objective sleep duration. Support (if any):

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin temperature) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue.