On indirect assessment of heart rate in video

Problem of indirect assessment of heart rate in video is addressed. Several methods of indirect evaluations (adaptive baselines) were examined on Remote Physiological Signal Sensing challenge. Particularly, regression models of dependency of heart rate on estimated age and motion intensity were obtained on challenge's train set. Accounting both motion and age in regression model led to top-quarter position in the leaderboard. Practical value of such adaptive baseline approaches is discussed. Although such approaches are considered as non-applicable in medicine, they are valuable as baseline for the photoplethysmography problem.

Remote Photoplethysmography: Rarely Considered Factors

Remote Photoplethysmography (rPPG) is a fast-growing technique of vital sign estimation by analyzing video of a person. Several major phenomena affecting rPPG signals have been studied (e.g. video compression, distance from person to camera, skin tone, head motions). However, to develop a highly accurate rPPG method, new, minor, factors should be investigated. First considered factor is irregular frame rate of video recordings. Despite of PPG signal transformation by frame rate irregularity, no significant distortion of PPG signal spectra was found in the experiments. Second factor is rolling shutter effect which generates tiny phase shift of the same PPG signal in different parts of the frame caused by progressive scanning. In particular conditions effect of this artifact could be of the same order of magnitude as physiologically caused phase shifts. Third factor is a size of temporal windows, which could significantly influence the estimated error of vital sign evaluation. It follows that one should account difference in size of processing windows when comparing rPPG methods. Short series of experiments were conducted to estimate importance of these phenomena and to determine necessity of their further comprehensive study.

Architectural Tricks for Deep Learning in Remote Photoplethysmography

Architectural improvements are studied for convolutional network performing estimation of heart rate (HR) values on color signal patches. Color signals are time series of color components averaged over facial regions recorded by webcams in two scenarios: Stationary (without motion of a person) and Mixed Motion (different motion patterns of a person). HR estimation problem is addressed as a classification task, where classes correspond to different heart rate values within the admissible range of [40; 125] bpm. Both adding convolutional filtering layers after fully connected layers and involving combined loss function where first component is a cross entropy and second is a squared error between the network output and smoothed one-hot vector, lead to better performance of HR estimation model in Stationary and Mixed Motion scenarios.