# Camera-Based Vital Sign Estimation Techniques and Mobile App Development
## 1 Introduction
However, although these recent methods demonstrate good performance, their effectiveness on video sequences captured under various lighting conditions has not been evaluated. Because the conventional contact-based pulse oximeter measurement method uses a clip-on or fixed sensor device, it causes discomfort to patients. In addition, because COVID-19 patients require frequent and accurate blood oxygen saturation (SpO2) monitoring, the demand for noncontact measurement is increasing. Recently, a noncontact SpO2 measurement method was proposed, based on rPPG signals extracted from images of human faces [13; 14; 15]. Changes in the blood concentration due to the amount of oxygen transported by hemoglobin in red blood cells result in changes in the wavelength of the reflected light captured by a photodiode or camera. This is because the absorption characteristics of the light wavelengths of oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) are different. At each illumination wavelength, the absorption of Hb differs from that of HbO2, and the SpO2 is estimated by measuring the absorption of blood at both wavelengths. Among the various light wavelengths, red (R, 660 nm) and IR (940 nm) light penetrate tissue and blood well; therefore, red and IR light are primarily used in PPG sensors. [PERSON] et al. [13] measured the SpO2 using an optical filter that only let through green and orange wavelengths in front of a camera. [PERSON] et al. [14] measured the SpO2 from R and G images obtained using a general RGB camera without a separate optical filter. And [PERSON] et al. [15] proposeda CNN-based SpO2 detection technique that utilizes DC and AC components from RGB signals of facial images. Recently, since the methods of simultaneously measuring the HR and respiratory rate (RR) using only an RGB or thermal camera are unreliable, bio-signal detection studies using both visible and thermal cameras have been conducted. [PERSON] et al. [16] proposed a noncontact bio-signal measurement method in which the HR was extracted from a green (G) image of the face ROI taken using an RGB camera and the RR was extracted from the thermal signal of the area around the nose obtained using an infrared thermal camera. However, this method is not suitable for long-term respiration monitoring because the nose region is not always in the thermal camera's field of view. [PERSON] et al. [17] proposed a method of measuring the HR from images of the face area and the RR from images of the chest area taken using a single RGB camera. [PERSON] et al. [18] measured the RR by analyzing the change in brightness over time in the chest area using an RGB camera. In the chest region, intensity signals were extracted by averaging all the pixels in the vertical direction for the RGB channels. Since these signals included motion components caused by respiration and noise, a motion vector was obtained using an optical flow method to remove noise and extract the motion components. The time-varying motion vector was converted into the power spectral density (PSD), and after using a BPF for the 0.05 to 2.0 Hz frequency band corresponding to respiration, the frequency with the maximum amplitude was measured as the RR. Table 1 shows a comparison of the experimental conditions used when testing various noncontact HR detection methods. Except for in tests of the proposed method, only one device was used, and all experiments except those using the POS and proposed methods were conducted for only one lighting situation and a limited age group. Our study used images taken using various mobile devices and under various lighting conditions to ensure the universality of the research results. Therefore, our approach lays important groundwork for ensuring that future noncontact healthcare algorithms maintain consistent accuracy when using various devices and under various lighting conditions.