## 3 A Taxonomy of Measuring Methods, Techniques, and Sensors in Physiological-Based DMSs
### Photoplethysmography (PPG)
#### 3.2.3 Camera-Based PPG  
An alternative variation of the classical PPG method is remote oximetry, which operates on the same principle as contact-based pulse oximetry. The principle of PPG measurement with reflected light can be extended to remote monitoring by a camera with a direct line of sight and a suitable light source of visible and/or near-infrared light (Figure 3f). The camera-based PPG method, also known as Image PPG (iPPG), attracts ongoing scientific interest. The iPPG method is based on the fact that when the heart muscle contracts, the skin color changes due to the pulsation of the blood. The camera captures a video stream of the driver's face images of the reflected light modulated by the change in skin color. From the captured subtle variations in skin color and cyclic body movements, respectively from the intensity of individual pixels, values of HR, HRV, and RR parameters can be calculated and monitored. Usually, the camera is placed in front of the driver on the windshield, on the center dashboard, or on the rearview mirror. In 2014 and 2017, [PERSON] et al. [55; 56] reported results on iPPG monitoring of heart and respiratory rates using RGB image processing and independent component analysis (ICA) in the visible light range. They used a web camera capturing facial images and applied FFT to calculate HR and RR values. While the earlier publication focused on laboratory experiments, the later publication included pilot measurements with one participant while driving a car. The results showed that the accuracy of the measurements depends on the ambient light and the distance between the camera and the driver's face. In 2014, [PERSON] et al. [57] used the same method to continuously measure HRV in real-world driving conditions. A standard web camera captured a video stream (15 fps, 640 \(\times\) 480 resolution) of the driver's face and used it to calculate HRV. The facial image was divided into seven sub-regions, and an average HRV value was calculated from them using the ICA method applied to each region. In 2017, [PERSON] et al. [58] presented an approach to measure the driver's heart rate in a car by using RGB cameras. The results from laboratory experiments and test drives using an empirical decomposition algorithm to filter out motor artefacts were presented. A machine learning algorithm with a k-nearest neighbor (kNN) classifier was applied to the color features, thus receiving the accuracy of HR measurement approaching medical-grade devices. Similarly, in 2017, [PERSON] et al. [59] presented a web-camera-based online heart rate monitoring system for in-car applications. In laboratory conditions at rest and with constant ambient light, they reported an achieved sensitivity of 93%, but in real-world driving conditions the sensitivity was reduced to 84% due to motor disturbances. Superimposed noise is caused by changing light, frequent sharp braking and acceleration, and vibrations from uneven road surfaces. They identified the forehead as a suitable measurement region of interest because it is minimally affected by facial grimaces, which cause additional motor artefacts. In 2018, [PERSON] et al. [60] conducted a study to obtain the pulse wave from RGB facial images through the changes in hemoglobin concentration over time. They used the ICA method to separate the RGB camera facial image into three components by the values of melanin pigment, hemoglobin pigment, and shadow intensity. In 2021, [PERSON] et al. [61] conducted a study with eight subjects for two weeks, filming their daily routes and capturing HRV data from facial images to assess the mental state of drivers. They used a web camera placed on the center dashboard, recording the driver's face. After applying neural network algorithms, they reported 3.4% more accurate results compared to similar algorithms in other publications. In the last decade, the use of active IR cameras (near-infrared--NIR) has also been proposed for use in DMSs.