Heart Rate Detection Using Microsoft Kinect: Validation and Comparison to Wearable Devices (original) (raw)
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Non-contact Heart Rate Monitoring Using Multiple RGB Cameras
International Conference on Computer Analysis of Images and Patterns CAIP 2019: Computer Analysis of Images and Patterns, 2019
Recent advances in computer vision and signal processing are enabling researchers to realize mechanisms for the remote monitoring of vital signs. The remote measurement of vital signs, including heart rate (HR), Heart Rate Variability (HRV), and respiratory rate, presents important advantages for patients. For instance, continuous remote monitoring alleviates the discomfort due to skin irritation and/or mobility limitation associated with contact-based measurement techniques. Recently, several studies presented methods to measure HR and HRV by detecting the Blood Volume Pulse (BVP) from the human skin. They use a single camera to capture a visible segment of the skin such as face, hand, or foot to monitor the BVP. We propose a remote HR measurement algorithm that uses multiple cameras to capture the facial video recordings of still and moving subjects. Using Independent Component Analysis (ICA) as a Blind Source Separation (BSS) method, we isolate the physiological signals from noise in the RGB facial video recordings. With respect to the ECG measurement ground truth, the proposed method decreases the RMSE by 18% compared to the state-of-the-art in the subject movement condition. The proposed method achieves an RMSE of 1.43 bpm and 0.96 bpm in the stationary and movement conditions respectively.
2018 26th European Signal Processing Conference (EUSIPCO), 2018
Technologies for contactless Heart Rate measurement support the progress in the diagnostic and healthcare fields, opening new possibilities even for everyday use at home. Among them, Videoplethysmography based on the Eulerian Video Magnification method has been already validated as an effective alternative to traditional, but often bulky, Electrocardiographic acquisitions. In this paper we study the influence of different measurement parameters on the Heart Rate estimation, in order to assess the reliability of the Videoplethysmography detection method under varying conditions, like different dimensions and positions of the processed regions of interest, pyramidal decomposition levels, and light conditions.
Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis
Sensors, 2016
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.
Contactless Real-Time Vital Signs Monitoring Using a Webcam
Measurement of vital signs such as heart rate (HR), respiratory rate (RR), blood pressure (BP), and oxygen saturation (SpO 2) is important for everyone in recent years due to the spread of epidemic diseases such as SARS and Coronavirus disease-19. This study presents methods to measure the four vital signs (HR, RR, BP, and SpO 2) simultaneously, contactless, and in real-time from facial video using a webcam. The estimation of the four vital signs in our study is based on photoplethysmography (PPG) extracted from the skin. There are many studies that dealt with the measurement of vital signs from PPG, but our study was distinguished from them by estimating and monitoring the four vital signs together in realtime and in less initial time takes 6 s only with good results where the maximum error is:
A Non-Contact Photoplethysmography Technique for the Estimation of Heart Rate via Smartphone
Applied Sciences, 2019
This paper describes the development of an application for mobile devices under the iOS platform which has the objective of monitoring patients with alterations or affections from cardiac pathologies. The software tool developed for mobile devices provides a patient and a specialist doctor the ability to handle and treat disease remotely while monitoring through the technique of non-contact photoplethysmography (PPG). The mobile application works by processing red, green, and blue (RGB) color video images on a specific region of the face, thus obtaining the intensity of the pixels in the green channel. The results are then processed using mathematical algorithms and Fourier transform, moving from the time domain to the frequency domain to ensure proper interpretation and to obtain the pulses per minute (PPM). The results are favorable because a comparison of the results was made with respect to the application of a medical-grade pulse-oximeter, where an error rate of 3% was obtained...
Measurement, 2019
The paper presents a feasibility study for heart rate measurement using a digital camera to perform health monitoring. The feasibility study investigates the reliability of the state of the art heart rate measuring methods in realistic situations. Therefore, an experiment was designed and carried out on 45 subjects to investigate the effects caused by illumination, motion, skin tone, and distance variance. The experiment was conducted for two main scenarios; human-computer interaction scenario and health monitoring scenario. The human-computer scenario investigated the effects caused by illumination variance, motion variance, and skin tone variance. The health monitoring scenario investigates the feasibility of health monitoring at public spaces (i.e. airports, subways, malls). Five state of the art heart rate measuring methods were re-implemented and tested with the feasibility study database. The results were compared with ground truth to estimate the heart rate measurement error. The heart rate measurement error was analyzed using mean error, standard deviation; root means square error and Pearson correlation coefficient. The findings of this experiment inferred promising results for health monitoring of subjects standing at a distance of 500 cm.
Contactless and Hassle Free Real Time Heart Rate Measurement with Facial Video
Journal of Cardiac Critical Care TSS
In the era of automation, an automatic and noninvasive tool is required for day-to-day health monitoring. Cardiac pulse being one of the vital physiological parameters has drawn attention of researchers for its measurement. In this article, the authors present an approach for automatic cardiac pulse measurement, using a typical webcam. In the presented approach, face region is detected and RGB traces are retrieved for the face region. FFT is applied on the extracted traces to compute power spectrum of the respective traces. The peaks of the power spectrum limited in the band region (0.75–4 Hz) are investigated to provide cardiac pulse measurement. The presented approach is hassle free in nature, and hence, can be adopted even in home environment. Experimental results represent the efficacy of the proposed approach.
ABSTRACTRegular monitoring of common physiological signs, including heart rate, blood pressure, and oxygen saturation, can be an effective way to either prevent or detect several potential conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 31% of all deaths worldwide are from CVDs. Recently, the COVID-19 pandemic has increased the interest in remote monitoring. At present, contact devices are required to extract most of an individual’ s physiological information, which can be inconvenient for users and may cause discomfort. However, remote photoplethysmography (rPPG) technology offers a solution for this issue, which enables contactless monitoring of the blood volume pulse signal using a regular camera, and ultimately can provide the same physiological information as a contact device. In this paper we propose an evaluation of rPPG technology against medical devices in a clinical environment, with a variety o...
Contactless Heart Rate Measurements using RGB-camera and Radar
2020
The detection of vital parameters with traditional approaches, as the electrocardiograph, requires to appropriately place electrodes in direct contact with patients' skin, often causing irritation. On the other hand, contactless measurement of physiological parameters provides an unobtrusive and comfortable instrument for subjects' conditions monitoring, with application to home monitoring of aging people and in particular to those suffering of heart disease. In this paper two contactless techniques are proposed, based on radar technology and on video processing from an RGB camera. In order to validate their precision, the proposed methods are compared with three wearable low cost devices, taken as a reference for the outcomes. The developed approaches prove to achieve excellent performances, with an estimated mean relative error of 0.55% with respect to a commercial cardiac strap device.