Abstract
In this paper we present a novel health monitoring method by estimating the heart rate and respiratory rate using an RGB camera. The heart rate and the respiratory rate are estimated from the photoplethysmography (PPG) and the respiratory motion. The method mainly operates by using the green spectrum of the RGB camera to generate a multivariate PPG signal to perform multivariate de-noising on the video signal to extract the resultant PPG signal. A periodicity based voting scheme (PVS) was used to measure the heart rate and respiratory rate from the estimated PPG signal. We evaluated our proposed method with a state of the art heart rate measuring method for two scenarios using the MAHNOB-HCI database and a self collected naturalistic environment database. The methods were furthermore evaluated for various scenarios at naturalistic environments such as a motion variance session and a skin tone variance session. Our proposed method operated robustly during the experiments and outperformed the state of the art heart rate measuring methods by compensating the effects of the naturalistic environment.
Highlights
Remote health monitoring is a rapidly growing research field
The heart rate measurement combined with modalities like EEG and MRI can result in improved accuracy of the diagnosis or analysis of brain conditions
In this paper, a health monitoring method was proposed by remotely measuring the heart rate and respiratory rate from the PPG signal
Summary
Remote health monitoring is a rapidly growing research field. Remote heart rate measurement using RGB camera have contributed vastly towards the growth of remote health monitoring. Poh et al [1] in 2010 reported the first facial video-based remote heart rate measuring method by using blind source separation (BSS) approach to estimate the photoplethysmography (PPG) signal from the red, green and blue spectrum of the RGB camera. Later researchers [2,3,4,5,6,7, 13, 32] presented a number of studies to improve the PPG signal quality by proposing temporal filtering methods, ROI selection and tracking methods, as well as heart rate estimation methods
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