Abstract
Heart rate has been measured comfortably using a camera without the skin-contact by the development of vision-based measurement. Despite the potential of the vision-based measurement, it has still presented limited ability due to the noise of illumination variance and motion artifacts. Remote ballistocardiography (BCG) was used to estimate heart rate from the ballistocardiographic head movements generated by the flow of blood through the carotid arteries. It was robust to illumination variance but still limited in the motion artifacts such as facial expressions and voluntary head motions. Recent studies on remote BCG focus on the improvement of signal extraction by minimizing the motion artifacts. They simply estimated the heart rate from the cardiac signal using peak detection and fast fourier transform (FFT). However, the heart rate estimation based on peak detection and FFT depend on the robust signal estimation. Thus, if the cardiac signal is contaminated with some noise, the heart rate cannot be estimated accurately. This study aimed to develop a novel method to improve heart rate estimation from ballistocardiographic head movements using the unsupervised clustering. First, the ballistocardiographic head movements were measured from facial video by detecting facial points using the good-feature-to-track (GFTT) algorithm and by tracking using the Kanade–Lucas–Tomasi (KLT) tracker. Second, the cardiac signal was extracted from the ballistocardiographic head movements by bandpass filter and principal component analysis (PCA). The relative power density (RPD) was extracted from its power spectrum between 0.75 Hz and 2.5 Hz. Third, the unsupervised clustering was performed to construct a model to estimate the heart rate from the RPD using the dataset consisting of the RPD and the heart rate measured from electrocardiogram (ECG). Finally, the heart rate was estimated from the RPD using the model. The proposed method was verified by comparing it with previous methods using the peak detection and the FFT. As a result, the proposed method estimated a more accurate heart rate than previous methods in three experiments by levels of the motion artifacts consisting of facial expressions and voluntary head motions. The four main contributions are as follows: (1) the unsupervised clustering improved the heart rate estimation by overcoming the motion artifacts (i.e., facial expressions and voluntary head motions); (2) the proposed method was verified by comparing with the previous methods using the peak detection and the FFT; (3) the proposed method can be combined with existing vision-based measurement and can improve their performance; (4) the proposed method was tested by three experiments considering the realistic environment including the motion artifacts, thus, it increases the possibility of the non-contact measurement in daily life.
Highlights
Heart rate is an important and popular indicator to monitor cardiac activity
The four main contributions are as follows: (1) the unsupervised clustering improved the heart rate estimation by overcoming the motion artifacts; (2) the proposed method was verified by comparing with the previous methods using the peak detection and the fast fourier transform (FFT); (3) the proposed method can be combined with existing vision-based measurement and can improve their performance; (4) the proposed method was tested by three experiments considering the realistic environment including the motion artifacts, it increases the possibility of the non-contact measurement in daily life
The contribution of this study can be summarized as follows: (1) the unsupervised clustering improved the heart rate estimation by overcoming the motion artifacts; (2) the proposed method was verified by comparing with the previous methods using the peak detection and the FFT; (3) the proposed method can be combined with existing vision-based measurement and can improve their performance; (4) the proposed method was tested by three experiments considering the realistic environment including the motion artifacts, it increases the possibility of the non-contact measurement in daily life
Summary
Heart rate is an important and popular indicator to monitor cardiac activity It was traditionally measured by skin-contact sensors such as electrocardiography (ECG), photoplethysmography (PPG), and ballistocardiography (BCG). They are accurate methods of standardized measurement, it is often difficult to wear for a long period of time in order to avoid inconvenience, measurement burden, and skin damages [1]. Since the vision technology is advanced, the heart rate has been measured from facial videos without the skin-contact. It allows the possibility of the vision-based measurement of heart rate in daily life by reducing the measurement burden. The main issues regarding the vision-based measurement are to improve signal-to-noise ratio (SNR) and to overcome the noise of illumination variance and motion artifacts [2]
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