A Simple Non-Invasive Automated Heart Rate Monitoring System Using Facial Images

2016 ◽  
pp. 100-122 ◽  
Author(s):  
Humaira Nisar ◽  
Zhen Yao Lim ◽  
Kim Ho Yeap
Keyword(s):  
Heart Rate ◽  
Power Spectrum ◽  
Color Model ◽  
Heart Rate Monitoring ◽  
Monitoring Method ◽  
Non Invasive ◽  
Average Accuracy ◽  
Contact Devices ◽  
The Face ◽  
Cie Lab

In this chapter we will discuss a simple non invasive automated heart rate monitoring method. Commonly heart rate is measured by using heart rate monitor devices. Many patients do not feel comfortable when they use contact devices for diagnostic purposes. Our algorithm gives a non-invasive way of heart rate measurement. The first step is to record a video. After 5 frames of the video are captured, the face is detected. A total of 300 frames will be used for further processing. At this stage, ROI (part of forehead) will be cropped out automatically. All image frames are in RGB color model, so these will be separated into 3 channels. For analysis, graph normalization is applied, which uses mean and standard deviation. Fast Fourier transform is used to plot the power spectrum of the traces. This power spectrum will have a peak if the heart rate is detected. We used RGB, HSI, YCbCr, YIQ, and CIE LAB color models for analysis. The best result is achieved with RGB color model followed by CIELab. The average accuracy is 95.32%.

A Non Invasive Heart Rate Measurement System for Multiple People in the Presence of Motion and Varying Illumination

2016 ◽  
Vol 1 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Humaira Nisar ◽  
Muhammad Burhan Khan ◽  
Wong Ting Yi ◽  
Yeap Kim Ho ◽  
Lai Koon Chun
Keyword(s):  
Heart Rate ◽  
Measurement System ◽  
Region Of Interest ◽  
Peak Frequency ◽  
Percentage Error ◽  
Color Model ◽  
Rate Measurement ◽  
Non Invasive ◽  
The Face ◽  
Band Pass

A real time non-invasive heart rate (HR) measurement system for multiple people in an image has been proposed in this paper. The data has been gathered using a webcam with different distances with the subjects under varying illumination conditions. The effect of motion of the subjects on HR measurement is also observed. The data is gathered from the face and cheeks are selected as the region of interest (ROI). RGB color model is used for processing. Fast Fourier transform is used to detect the peak frequency after band pass filtering. The green channel of RGB color model gives best HR measurement. The minimum percentage error of 3.1% is achieved in the presence of slow movement and multiple persons at the relative distance of 100 cm.

scholarly journals Analysis of Non-invasive Video Based Heart Rate Monitoring System obtained from Various Distances and Different Facial Spot

2018 ◽  
Vol 1049 ◽  
pp. 012003 ◽  
Author(s):  
Norwahidah Ibrahim ◽  
Razali Tomari ◽  
Wan Nurshazwani Wan Zakaria ◽  
Nurmiza Othman
Keyword(s):  
Heart Rate ◽  
Monitoring System ◽  
Heart Rate Monitoring ◽  
Non Invasive

scholarly journals Real-Time Cardiac Beat Detection and Heart Rate Monitoring from Combined Seismocardiography and Gyrocardiography

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3472 ◽  
Author(s):  
D’Mello ◽  
Skoric ◽  
Xu ◽  
Roche ◽  
Lortie ◽  
...  
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Time Algorithm ◽  
Cardiac Monitoring ◽  
Computational Time ◽  
Heart Rate Monitoring ◽  
Heart Rates ◽  
Non Invasive ◽  
Beat Detection ◽  
Speed Accuracy

Cardiography is an indispensable element of health care. However, the accessibility of at-home cardiac monitoring is limited by device complexity, accuracy, and cost. We have developed a real-time algorithm for heart rate monitoring and beat detection implemented in a custom-built, affordable system. These measurements were processed from seismocardiography (SCG) and gyrocardiography (GCG) signals recorded at the sternum, with concurrent electrocardiography (ECG) used as a reference. Our system demonstrated the feasibility of non-invasive electro-mechanical cardiac monitoring on supine, stationary subjects at a cost of $100, and with the SCG–GCG and ECG algorithms decoupled as standalone measurements. Testing was performed on 25 subjects in the supine position when relaxed, and when recovering from physical exercise, to record 23,984 cardiac cycles at heart rates in the range of 36–140 bpm. The correlation between the two measurements had r2 coefficients of 0.9783 and 0.9982 for normal (averaged) and instantaneous (beat identification) heart rates, respectively. At a sampling frequency of 250 Hz, the average computational time required was 0.088 s per measurement cycle, indicating the maximum refresh rate. A combined SCG and GCG measurement was found to improve accuracy due to fundamentally different noise rejection criteria in the mutually orthogonal signals. The speed, accuracy, and simplicity of our system validated its potential as a real-time, non-invasive, and affordable solution for outpatient cardiac monitoring in situations with negligible motion artifact.

An Effective Algorithm for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms

2020 ◽  
Vol 10 (3) ◽  
pp. 633-640 ◽  
Author(s):  
Jingxian Liang ◽  
Jialin Huang ◽  
Liwei Mu ◽  
Baoxian Yu ◽  
Pengbin Chen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Cross Correlation ◽  
Effective Algorithm ◽  
Sleep Staging ◽  
Heart Rate Monitoring ◽  
Non Invasive ◽  
Step Procedure ◽  
The Mean ◽  
Beat Detection ◽  
Beat Heart

Ballistocardiograms (BCG) is an essential signal for vital sign monitoring. Obtaining the beat-to-beat intervals from BCG signal is of great significance for home-care applications, such as sleep staging, heart disease alerting, etc. The current approaches of detecting beat-to-beat intervals from BCG signals are complex. In this paper, we develop a non-invasive BCG monitoring system, and propose an effective and accurate algorithm for beat-to-beat detection. Firstly, a heartbeat shape is adaptively modeled based on a two-step procedure by taking advantage of the J-peak and the K-valley of BCG signals. Then, forward and backward detections with the criteria of both the morphological distance and the cross-correlation are jointly employed to find the position of each BCG signal, and in turn, to determine the beat-to-beat intervals of BCG signals. The proposed method was validated in at least 90 minutes recording from 10 subjects in various setups. The mean absolute beat-to-beat intervals error was 10.72 ms and on an average 97.93% of the beat-to-beat intervals were detected.

scholarly journals Non-contact measurement of emotional and physiological changes in heart rate from a webcam

2017 ◽  
Author(s):  
Christopher R. Madan ◽  
Tyler Harrison ◽  
Kyle E. Mathewson
Keyword(s):  
Heart Rate ◽  
Physiological Stress ◽  
Physiological State ◽  
Stimulus Presentation ◽  
The Body ◽  
Physiological Measures ◽  
Non Invasive ◽  
The Face ◽  
High Level ◽  
Electrocardiogram Ecg

AbstractHeart rate, measured in beats per minute (BPM), can be used as an index of an individual’s physiological state. Each time the heart beats, blood is expelled and travels through the body. This blood flow can be detected in the face using a standard webcam that is able to pick up subtle changes in color that cannot be seen by the naked eye. Due to the light absorption spectrum of blood, we are able to detect differences in the amount of light absorbed by the blood traveling just below the skin (i.e., photoplethysmography). By modulating emotional and physiological stress—i.e., viewing arousing images and sitting vs. standing, respectively—to elicit changes in heart rate, we explored the feasibility of using a webcam as a psychophysiological measurement of autonomic activity. We found a high level of agreement between established physiological measures, electrocardiogram (ECG), and blood pulse oximetry, and heart rate estimates obtained from the webcam. We thus suggest webcams can be used as a non-invasive and readily available method for measuring psychophysiological changes, easily integrated into existing stimulus presentation software and hardware setups.

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