Remote heart rate estimation by evaluating measurements from multiple signals

Abstract: Heart rate can say a lot about a person's health. While most conventional methods for heart rate measurement require contact with the subject, these are not always applicable. In this thesis, a non-invasive method for pulse detection is implemented and analyzed. Different signals from the color of the forehead—including the green channel, the hue channel and different ICA and PCA components—are inspected, and their resulted heart rates are weighted together according to the significance of their FFT peaks. The system is tested on videos with different difficulties regarding the amount of movement and setting of the scene. The results show that the approach of weighting measurements from different signals together has great potential. The system in this thesis, however, does not perform very well on videos with a lot of movement because of motion noise. Though, with better, less noisy signals, good results can be expected.