Improvements in remote video based estimation of heart rate variability using the Welch FFT method

Non-contact heart rate and heart rate variability measurements have 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 five-band cameras: (1) an adaptive non-rectangular region of interest identified using automatically detected facial feature points, (2) improved peak detection within the blood volume pulse (BVP) signal, and (3) 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.