## 3 Measurement of State Data Based on Non-Contact Image Analysis
### Optical Blood Flow (Heart Rate) Signal Acquisition
The optical blood flow signal extracted according to the Nyquist sampling theory can be analyzed up to the frequency band corresponding to the maximum '1/frame rate'. For instance, 30 fps video analysis up to 15 Hz. Since the normal human pulse rate is between 42-240 beats per minute, the frequency band of interest is set to the 0.7-4.0 Hz band to detect the band with the maximum peak. Figure 4: Removing low-frequency noise in the signal through signal normalization (**a**), removing high-frequency noise in the signal through bandpass filtering (**b**). In the power spectral density of the detected optical blood flow signal, factors such as respiration and motion noise are included. In the process of setting the frequency band of interest, the estimated pulse rate was within the effective pulse rate range by ignoring periodic components not related to the human pulse. The power spectral density of a physiological signal includes a fundamental frequency corresponding to the pulse rate and a harmonic frequency component that is an integer multiple of the source frequency. Pulse rate can be estimated through source frequency detection. When the frequency band with the detected maximum power was \(f_{max}\), (1) was used to convert it into beats per minute (BPM). \[\mathrm{HR}=f_{max}\times 60 \tag{1}\]  
For example, when the frequency band having the maximum power in a certain optical blood flow signal is 1.1, the average heart rate can be estimated as 66 bpm. In order to obtain heart rate variability (HRV) information for further analysis, it is necessary to measure the peak-to-peak interval (PPI) in the signal in the time series domain. In order to obtain heart rate variability (HRV) information for further analysis, it is necessary to measure the peak-to-peak interval (PPI) in the signal in the time series domain. A separate peak detector module was used for peak position detection, and constraints were used to detect peak intervals within the effective pulse rate range. The guaranteed distance between the minimum peaks is determined by 'fps/maximum pulse rate frequency', and the maximum pulse rate is a variable that can be adjusted to suit the application scenario. For PPI calculation, position information of the peaks was stored in a separate array, and the timestamp difference value of the two most recent peaks was calculated as the current PPI. The resolution is determined according to the frame rate, and considering 30 FPS (Frames Per Second), it has a resolution of about 2 BPM in the pulse section at rest and about 8 BPM in the high heart rate section. Considering 60 FPS, it can have a resolution of about 1 BPM in the pulse section at rest and about 4 BPM in the high heart rate section. Recently released general webcams have a performance of about 30 FPS in an uncompressed format with a resolution of 640 \(\times\) 480 pixels, but detailed analysis of heart rate variability is possible depending on the performance conditions of the camera used. Heart rate variability refers to a periodic change in heart rate and can be used to estimate stress status and health status through additional analysis. In addition, in the case of healthy people, the heart rate variability is irregular and complex in order to achieve a physiological balance in a short time by responding sensitively to changes, but the reduction in heart rate variability indicates that the dynamic changes and complexity of the heart rate has decreased. It was confirmed that the body's ability to adapt has decreased as shown in Figure 6.  
Figure 5: Band detection and analysis with maximum peak for calculating average pulse rate. In order to extract physiological parameters from the signal, a window of a certain size is covered to estimate the parameters for the corresponding signal section. In this case, a sliding window method was used to extract continuous physiological parameters in real time.