MUNENORI FUKUNISHI, KDANIEL McDUFF AND NORIMICHI TSUMURA
Artificial Life and Robotics 2017 (accepted)
Abstract
Non-contact heart rate and heart rate variability measurement has applications
in healthcare and affective computing. Recently, a system utilizing a five-band
camera (RGBCO: red, green, blue, cyan, orange) was proposed, and shown
to improve both remote measurement of heart rate and heart rate variability
over an RGB camera. In this paper, we propose an improved method for video-based
estimation of heart rate variability. We introduce three advancements over
previous work utilizing fiveband cameras: (i) an adaptive non-rectangular
region of interest identified using automatically detected facial feature
points, (ii) improved peak detection within the blood volume pulse (BVP)
signal, (iii) improved HRV calculation using the Welch periodogram. We
apply our method to a test dataset of subjects at rest and under cognitive
stress and show qualitative improvements in the stability of HRV spectrogram
estimation. Although we evaluate our method using a five-band camera the
method could be applied to video recorded with any camera.
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