Dynamic heart rate estimation using facial images from video sequences / Yu Yong Poh

• Main Author: Yu, Yong Poh
• Format: Thesis
• Published: 2016
• Subjects: T Technology (General)
• Online Access: http://studentsrepo.um.edu.my/6649/7/yong_poh.pdf; http://studentsrepo.um.edu.my/6649/
• Institution: Universiti Malaya

Video images have been widely used to extract relevant information for different applications. One of the applications is the heart rate estimation using facial images from video sequences. Previous studies have focused only on heart rates that do not vary much throughout the entire video duration. However, dynamic heart rate variation is of interest since it may provide necessary information for daily application. For instance, in individual sports games such as cycling, badminton and tennis, knowing the dynamic heart rates of a player while carrying out an activity will be useful in determining the level of fatigue of that player. In this thesis, novel approaches are developed to estimate dynamic heart rate readings using facial images from video sequences. A challenge for dynamic heart rate estimation is to determine the shortest duration or length of the video sequence without compromising the accuracy of heart rate readings. To address this issue, this thesis reports two approaches: 1) Independent component analysis (ICA) combined with mutual information, 2) the decorrelation of the color components in log-space. In the first approach, ICA is used to recover the heart rate source from the color components of facial images. An important consideration in using short video sequences is that the ICA sources may have insufficient independence among themselves. Without determining the independence of the sources, there is a possibility of the heart rate signal combining with other signals to render an inaccurate reading. Hence in this study, mutual information is integrated with ICA to determine the shortest video duration needed for estimating dynamic heart rate readings accurately. In the second approach, principal component analysis (PCA) is used to recover the uncorrelated signals, including the heart rate signals. From the studies, it is found that the set of color components, namely red, green, and blue, in log-space, are correlated to each other. The principal components may have insufficient uncorrelatedness among themselves when the video duration is too short. Hence, PCA is combined with the Pearson correlation coefficient to determine the iv shortest video duration that still gives acceptable accuracy. Two experiments are carried out to validate the proposed approaches. A camcorder is used to capture the facial images of seven subjects. The first experiment involves the measurement of subjects’ increasing heart rates while cycling whereas the second experiment involves falling heart beats. All estimated heart rate readings are compared with readings obtained from Polar Team2 Pro. Polar Team2 Pro samples and computes the instantaneous heart rate by measuring at least one electrocardiogram (ECG) signal waveform. Overall experimental results show the proposed method can be used to measure dynamic heart rates where the root mean square error (RMSE) is less than 3 beats per minute (BPM) and the correlation coefficient is 0.99. The respective Bland-Altman plots for each approach indicate that only a small number of estimated heart rate readings are located outside the 95 % limit of agreement interval where the maximum error is less than 8 BPM.