A machine learning approach to improve contactless heart rate monitoring using a webcam

Blood perfusion display based on video pulse wave

In this study, an easy system for monitoring dynamic blood perfusion patterns and the pulse wave velocity (PWV) has been developed by processing video images of a human body to assess blood circulation for daily management of physical conditions or for detecting persons in poor physical condition in public places. The experiment suggested that this tool can be used to easily evaluate the PWV; however, the obtained value from the video image of the face was about 1/10 of the standard value calculated from thick vessels. This difference may be related to the difference between thick vessels and thin-branched arterioles.

Techniques for estimating blood pressure variation using video images

It is important to know about a sudden blood pressure change that occurs in everyday life and may pose a danger to human health. However, monitoring the blood pressure variation in daily life is difficult because a bulky and expensive sensor is needed to measure the blood pressure continuously. In this study, a new non-contact method is proposed to estimate the blood pressure variation using video images. In this method, the pulse propagation time difference or instantaneous phase difference is calculated between two pulse waves obtained from different parts of a subject's body captured by a video camera. The forehead, left cheek, and right hand are selected as regions to obtain pulse waves. Both the pulse propagation time difference and instantaneous phase difference were calculated from the video images of 20 healthy subjects performing the Valsalva maneuver. These indices are considered to have a negative correlation with the blood pressure variation because they approximate the pulse transit time obtained from a photoplethysmograph. However, the experimental results showed that the correlation coefficients between the blood pressure and the proposed indices were approximately 0.6 for the pulse wave obtained from the right hand. This result is considered to be due to the difference in the transmission depth into the skin between the green and infrared light used as light sources for the video image and conventional photoplethysmogram, respectively. In addition, the difference in the innervation of the face and hand may be related to the results.

Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human–machine interaction.

Computer-Vision-Guided Human Pulse Rate Estimation: A Review

Human pulse rate (PR) can be estimated in several ways, including measurement instruments that directly count the PR through contact- and noncontact-based approaches. Over the last decade, computer-vision-assisted noncontact-based PR estimation has evolved significantly. Such techniques can be adopted for clinical purposes to mitigate some of the limitations of contact-based techniques. However, existing vision-guided noncontact-based techniques have not been benchmarked with respect to a challenging dataset. In view of this, we present a systematic review of such techniques implemented over a uniform computing platform. We have simultaneously recorded the PR and video of 14 volunteers. Five sets of data have been recorded for every volunteer using five different experimental conditions by varying the distance from the camera and illumination condition. Pros and cons of the existing noncontact image- and video-based PR techniques have been discussed with respect to our dataset. Experimental evaluation suggests that image- or video-based PR estimation can be highly effective for nonclinical purposes, and some of these approaches are very promising toward developing clinical applications. The present review is the first in this field of contactless vision-guided PR estimation research.

Pulse rate variability analysis by video using face detection and tracking algorithms

Heart rate variability (HRV) is a physiological parameter which has increased its importance in recent years due to its relationship with the autonomic nervous system (ANS) and cardiovascular disorders. In this work the pulse rate variability (PRV) is measured as a surrogate measurement of HRV using only a video of the face of the subject. Because of pulse rate signals obtained by video are largely affected by the person movements and the analyzed region of interest (ROI), we propose the application of face detection and tracking algorithms as a solution to the artifacts produced by these factors. We compare the obtained results of the proposed method with a pulse sensor signal of a reference system. The statistical analysis results show good agreement between both methods.

An automatic method for arterial pulse waveform recognition using KNN and SVM classifiers

The measurement and analysis of the arterial pulse waveform (APW) are the means for cardiovascular risk assessment. Optical sensors represent an attractive instrumental solution to APW assessment due to their truly non-contact nature that makes the measurement of the skin surface displacement possible, especially at the carotid artery site. In this work, an automatic method to extract and classify the acquired data of APW signals and noise segments was proposed. Two classifiers were implemented: k-nearest neighbours and support vector machine (SVM), and a comparative study was made, considering widely used performance metrics. This work represents a wide study in feature creation for APW. A pool of 37 features was extracted and split in different subsets: amplitude features, time domain statistics, wavelet features, cross-correlation features and frequency domain statistics. The support vector machine recursive feature elimination was implemented for feature selection in order to identify the most relevant feature. The best result (0.952 accuracy) in discrimination between signals and noise was obtained for the SVM classifier with an optimal feature subset .