# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 3 Method
### Noise-Assessment-Based Screening Method  
Figure 4 presents the overall process of acquiring rPPG in an infrared environment. Initially, a 10 s video with a frame rate of 30 fps is utilized as the input. Subsequently, facial regions in the video are detected, and the region of interest (ROI) is selected. This pre-processing step is commonly employed by numerous algorithms, including Green and DistancePPG, for rPPG extraction. Following this, the average of pixel changes within the region is extracted as a signal, which then undergoes detrending and bandpass filtering within the heart rate range to eliminate motion and noise. The refined signal is consequently fed into algorithms such as Green or DistancePPG to extract the rPPG. Ultimately, the Fourier transform is employed to ascertain the most frequently occurring frequency value, which is expressed as the final predicted heart rate in terms of frequency. \begin{table}
\begin{tabular}{l l} \hline
**Camera (Image Sensor)** & **CM3-U3-13Y3M-CS (On Semi PYTHON 1300; Teledyne FLIR, Wilsonville, OR, USA) [19]** \\ \hline Infrared illuminator & EI-100 (irradiation angle: 70\({}^{\circ}\), DC 12 V 0.8 A 10 W; Nikon Instruments, Tokyo, Japan) \\ Contact PPG sensor & Uppulse 360 (LAXTHA, Daejeon, Republic of Korea) [20] \\ Image resolution/fps & 640 \(\times\) 512/30 fps \\ Experiment setup & Wild environment (head movements, facial expressions, and conversations allowed) \\ Illumination setup & Indoor environment with infrared illuminator \\ Number of subjects & 18 subjects (male: 9, female: 9) \\ Recording time & Total 144 min (72 samples, 2 min per sample) \\ \hline \end{tabular}
\end{table} 
Table 2: Dataset specification. Figure 3: Example of the data acquisition setup employed for collecting the dataset in the present study. The screening method was applied to the derived heart rate, and the overall process is depicted in Figure 5. In essence, the rPPG screening method utilizes only reliable heart rates among those predicted from the rPPG signal, with reliability determined by the image quality. Factors such as substantial motion or significant illumination changes within the image inevitably diminish the acquired signal's quality, making it challenging for the algorithm to compensate. Additionally, if the signal-to-noise ratio (SNR) value is exceedingly low, noise impact becomes disproportionately large compared to the signal. As a result, we implemented measures to assess whether the predicted heart rate--determined by detecting motion amount, light changes, and the SNR within the video--can be considered reliable. The judgment process is derived from various models, with the confidence level of the \(rPPG_{confidence}\) determined by the model. This allows for a numerical expression of the predicted BPM's reliability. If the derived \(rPPG_{confidence}\) exceeds a certain threshold (\(T\)), the predicted heart rate is utilized. Conversely, if it falls below \(T\), the derived heart rate is disregarded. This is illustrated in Equation (1). In this equation, \(k\) represents the total number of samples for the derived heart rate. The optimal \(T\) value was also determined through training. Subsequently, the derived \(rPPG_{k}(T)\) is used to perform the \(rPPG_{screening}\). This method employs only the predicted heart rates with \(rPPG_{confidence}(k)\) values greater than or equal to \(T\) among all predicted rPPGs. In Equation (2), \(K_{rPPG_{k}(T)>0}\) denotes the number of elements with \(rPPG_{confidence}(k)\) greater than or equal to \(T\) among the total number of \(K\) predictions. \(rPPG_{screening}\) signifies the average BPM post-filtering. This equation demonstrates the process of screening solely reliable heart rates from the extracted heart rates.