This document describes a study that investigated extracting heart rate from video data in a non-contact way. The study recorded video of 3 subjects' faces at rest, during physical activity, and after resting. It analyzed the videos to extract the heart rate signal from changes in skin color over time. It found that the extracted heart rates matched expectations, falling within normal ranges at rest, elevated during activity, and lowering after resting. The study demonstrated the feasibility of non-contact heart rate measurement via video analysis.

1. Heart Rate Measurement
from Video Data

2. Introduction
The measurement of a person's heart rate is done by using electrodes
and gels. Therefore, there exists some level of contact with patients.
However, in order to reduce the level of physical contact with a patient,
the technique of non-contact video based heart rate monitoring is
employed.
Non-contact video based heart rate monitoring is a quick and easy
method of heart rate measurement which seeks to reduce the level of
contact with patients. In addition, this method has tremendous potential
in efficient delivery of health care.

3. Introduction cont’d
This method is also a cheap way of obtaining heart rate measurements.
This method uses the fact that, there will variations of reflected light
from any area of reference on a person’s body, due to changes in blood
volume as a result of that person’s heart rate.
This project aims to investigate the feasibility of extracting one’s heart
rate from video data of that person.

4. Previous Work
Poh et al. (2014) performed heart rate measurements using automatic
face tracking and blind source separation to separate the colour channels
into independent components. Video files were obtained using a
standard laptop webcam. Subjects sit facing the webcam with the
camera focusing on their facial region. All videos were obtained in
colour with a frame rate of 15 frames per second. Blind source
separation was used as a method to reduce noise from the signal
obtained.

5. Previous Work cont’d
The processing of data was done using Matlab. The OpenCV face
detection algorithm was used to detect the faces of the subject's. The
region of interest was obtained then separated into its three RGB
channels (red, green, blue). However, the red and blue channels were
discarded as the green channel contained greater plethysmographic
signal. Independent Component Analysis was applied to this signal then
the Fourier transform. The pulse frequency corresponded to that
frequency component with the highest power in the spectrum.

6. Procedure
 Firstly, video files lasting between 10-15 seconds were obtained using a
high resolution video camera at a frame rate of 30 frames per second.
 Video files were obtained in an enclosed room with only one light source in
order to reduce any extraneous elements that may affect signal.
 These video files were then inputted into Matlab. The Viola-Jones face
detection algorithm were used to identify the pair of eyes found in the video.
 After detection of eye pair, the Region of Interest (ROI) was determined.
This was a portion of the person's forehead.

7. Procedure cont’d
 The video file was then separated into tits individual red, green and
blue channels. However, the red and blue channels were discarded.
 The green channel of each frame of the video was then processed. An
average intensity produced by each frame was then obtained which
was plotted against time. This was the time series data.
 A second order Butterworth band pass filter was then applied to the
time series data in order to obtain those frequencies which only
correspond to the heart rate signal.

8. Procedure cont’d
The signal was filtered between 0.75 and 4 Hz; this corresponded to a
heart rate of 40-230 beats per minute.
 The Fourier transform was then applied to this filtered signal using
FFT function in Matlab. The heart rate of the subject was determined
by the frequency component with highest amplitude.

9. Results
 Table 1 shows heart rate measurements for three different individuals
of different ages, complexion and sex for different conditions.
 Firstly, the resting heart rate of the individuals was recorded.
 Secondly, the individuals were then asked to do an activity which
would have their heart rates being increased.

10. Results cont’d
 The individuals did jumping jacks for approximately 5 minutes, after
which their heart rates were measured again.
 The final heart rate measurement was done after the individual’s heart
rate was back to normal, this was approximately after 5 minutes of
rest.

11. Results – Table 1
Volunteer
Resting Heart
Rate (bpm)
Heart Rate
after doing an
activity (bpm)
Heart Rate
after 5 mins
rest (bpm)
1 57.5 98 73.1
2 75.2 120.7 81.2
3 83.3 103.8 90.4

12. Analysis of Results
 For all three individuals, the resting heart rate measurement was
found within the normal range. This is between 40-230 beats per
minute.
 There was a similar trend observed for the heart rate measurement
after doing an activity. As expected, after doing any physical activity,
there will be an increase in a person’s heart rate. Likewise, There was
a change observed in the measurement from the algorithm used.

13. Analysis cont’d
These measurements still fell within the normal range of 40-230 beats
per minute. The slight increase however, was as a result of the jumping
jacks done by the individuals.
 After 5 minutes rest, it was expected that the individual’s heart rate
would regain normality once more. This was reflected accordingly in
the results obtained. The values obtained were not exactly the same as
the resting heart rate, but was lower than the values obtained just after
doing jumping jacks and were fairly close the resting heart rate.

14. Limitations
 Due to the fact that the algorithm was not very robust, the video files
obtained had to be very ideal. A lot of problems were encountered
when trying to obtain these ideal video files.
 Persons with a lighter complexion gave better results than persons of
dark complexion.

15. Improvements
 This experiment can be improved by making the algorithm more
robust. Allow the algorithm to be able to deal with any extraneous
elements that may affect the results such as varying light sources or
any type of movement when recording the videos.
 The experiment could also be improved by creating an ideal
environment to obtain videos.

16. Discussion
 The aim of this experiment was to investigate the feasibility of
extracting heart rate from video data. The resting heart rate of humans
is found within the range of 40-230 beats per minute (bpm). One’s
heart rate can vary according to a person’s athleticism, age, sex
amongst other factors. All resting heart rate measurements were found
in the range of 50-85 beats per minute which is normal. In addition, it
is expected that any physical activity will increase a person’s heart
rate. An increase in the heart rate of the individuals was also evident.
This increase was found in the range of 98-104 bpm which is also

17. Discussion cont’d
normal. Finally, After resting, it is expected a person’s heart rate went
back to their regular beats per minute. However, this was not observed
within results, the heart rates ascertained were a bit higher than the
original resting heart rate measured; but it is evident that it had
decreased and heading towards their normal heart rate.

18. Conclusion
 In conclusion, this project was very interesting, a lot of knowledge
was gained as it regards to programming, image and video processing
and the heart rate of humans. In addition, it can be said that the
extraction of heart rate from video data is a feasible method and in
future will become very useful.

19. References
Poh M, McDuff DJ, Picard RW. 2010. Non-contact, automated cardiac
pulse measurements using video imaging and blind source separation.
Optics Express [Internet]. [cited 2015 Apr 18]; 18 (10). Available from:
http://hdl.handle.net/1721.1/66243 rom