E-Health monitoring using camera: Measurement of vital parameters in a noisy environment

We investigated contact-less solution to study micro circulation and its spatial heterogeneity by means of RGB imaging and to estimate blood oxygenation and heart rate. Oxygen saturation SpO2 and heart rate HR are indicators directly related to the cardiovascular diseases. Detecting low level of oxygen and abnormal heart rate can be an indicator of fatal heart problem and require urgent medical attention. Traditionally, measurement of HR and SpO2 is made through the probes or pulse oximeter attached to the skin surface and hence could be a potential way of spreading contagious infections. For a non-contact assessment of pulse rate, imaging photoplethysmogram iPPG has been proposed, where data is acquired through the video frames and different image analysis-based methods are used to extract required information. We detects face from the sequence of images using Viola Jones algorithm, followed by the extraction of region of interest, filtering performed in spatial domain removed artifacts, and intensity signal gives iPPG estimation by using CHROM method. The estimation of HR and SpO2 from these signals are evaluated on publicly available data set containing facial videos and reference contact photoplethysmogram. Furthermore, we also investigate the performance of these methods on data acquired under diverse illumination conditions. Relative correlation and % error gives satisfactory results to extend the method for more diverse scenario

Robust heart-rate estimation from facial videos using Project_ICA.

‘This is an author-created, un-copyedited version of an article published in Physiological Measurement. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1361-6579/ab2c9f’OBJECTIVE: Remote photoplethysmography (rPPG) can achieve non-contact measurement of heart rate (HR) from a continuous video sequence by scanning the skin surface. However, practical applications are still limited by factors such as non-rigid facial motion and head movement. In this work, a detailed system framework for remotely estimating heart rate from facial videos under various movement conditions is described. APPROACH: After the rPPG signal has been obtained from a defined region of the facial skin, a method, termed 'Project_ICA', based on a skin reflection model, is employed to extract the pulse signal from the original signal. MAIN RESULTS: To evaluate the performance of the proposed algorithm, a dataset containing 112 videos including the challenges of various skin tones, body motion and HR recovery after exercise was created from 28 participants. SIGNIFICANCE: The results show that Project_ICA, when evaluated by several criteria, provides a more accurate and robust estimate of HR than most existing methods, although problems remain in obtaining reliable measurements from dark-skinned subjects

A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos

This paper presents a comparative evaluation of methods for remote heart rate estimation using face videos, i.e., given a video sequence of the face as input, methods to process it to obtain a robust estimation of the subjects heart rate at each moment. Four alternatives from the literature are tested, three based in hand crafted approaches and one based on deep learning. The methods are compared using RGB videos from the COHFACE database. Experiments show that the learning-based method achieves much better accuracy than the hand crafted ones. The low error rate achieved by the learning based model makes possible its application in real scenarios, e.g. in medical or sports environments.Comment: Accepted in "IEEE International Workshop on Medical Computing (MediComp) 2020

A Multimodal Approach for the Assessment of Alexithymia: An Evaluation of Physiological, Behavioral, and Self-Reported Reactivity to a Traumatic Event-Relevant Video

Evidence suggests alexithymia is often relatively elevated among people suffering from posttraumatic stress symptoms (PTSS). Despite a growing body of research supporting this relation between alexithymia and PTSS, it is unclear whether alexithymia is a unique predictor of emotional reactivity relative to posttraumatic stress symptoms. Furthermore, existing literature is largely limited to retrospective, self-reported symptoms. Therefore, the current study employed a multimodal assessment strategy for measuring emotional reactivity in the context of posttraumatic stress. More specifically, self-report, behavioral, and physiological measures were used to measure emotional responding to a traumatic event-related stimulus among motor vehicle accident victims. It was hypothesized that behavioral and self-reported responding would evidence a negative relation to level of alexithymia, while physiological responding was not expected to relate to levels of alexithymia. Results replicated previous research demonstrating a strong correlation between self-reported PTSS and alexithymia. Also as expected, alexithymia did not predict physiological responding to the stimulus. However, alexithymia was not found to uniquely predict self-reported or behavioral responding above and beyond the influence of PTSS. These findings do not conclusively support alexithymia as a unique predictor of emotional responding relative to PTSS