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.

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%.

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