## 2 Video Camera Imaging Based Method
### Motion Based Methods  
Cardiorespiratory activity causes subtle motion in various parts of the human body that can be measured from video to extract vital signs. Numerous researchers have introduced different techniques to monitor vital signs using information extracted from motion. [PERSON] et al. [49] proposed a contactless method based on optical flow analysis to extract RR from the video of whole body motion captured by a CCD (charge-coupled device) camera. Another optical flow analysis-based respiratory monitoring system was introduced by [PERSON] et al. [50] using videos of a subject's chest movement recorded by video camera. Optical flow-based techniques have been found to be susceptible to motion artefacts caused by the subject's movement, ambient light and unclear ROI. Computational complexity is a consideration for optical flow-based analysis compared to other processing options. Several studies have been published based on blind source separation (BSS) using either principal component analysis (PCA) or independent component analyses (ICA). [PERSON] et al. [51] proposed a novel noncontact method based on head motion using facial video recorded by a digital camera to measure heart rate. In their proposed method, the Viola-Jones (V-J) face detector [52] and the Kanade-Lucas-Tomasi (KLT) [53] tracking algorithm were exploited for detecting the face, extracting the ROI and tracking the ROI feature points based on the good feature tracking (GFT) method. Then, using a Butterworth filter, the tracked points were temporally filtered and principal component analysis (PCA) was exploited to remove artefacts and measure the physiological signal. Finally, the pulse rate was extracted by means of a simple peak detection algorithm followed by a Fast Fourier Transform (FFT). On the other hand, to monitor HRs, [PERSON] et al. [54] introduced another noncontact method using head motion captured by smart phone camera based on independent component analyses (ICA) rather than PCA. These methods are susceptible to motion artefacts as they considered only stationary subjects without any internal or external movement. To mitigate these limitations, an improved head motion-based method to extract HR using a webcam was designed by [PERSON] et al. [55] based on the discrete cosine transform (DCT) and a moving average filter considering various facial expressions and head poses. [PERSON] et al. [56] further improved this technique by integrating both the GFT method and supervised descent method (SDM) with training against the MANHOB-HCI (human computer interaction) database with moving objects. [PERSON] et al. [57] proposed a method based on tracking both background and facial features to solve the handshaking problem with a smartphone camera. This method was further enhanced in [58] by exploiting a system with two cameras in a smartphone, where the front camera was for tracking facial features and the rear camera was for tracking background features. Some researchers have used video magnification to extract vital signs from motion. [PERSON] et al. [59] introduced a contactless method combining both Eulerian video magnification (EVM) [60] and 2-Gaussian curve modelling for extracting pulse signals from a subject's wrist using a digital camera. A study by [PERSON] et al. [61] used video magnification and a frame subtraction method to remotely measure cardiac activity (heart rate, pulse width and the total cycle length) from head motion. Another study by [PERSON] et al. [62] presented a noncontact respiratory monitoring scheme for detecting and measuring respiratory rates as well as respiratory cycle timing parameters by using a video camera from the movement of the chest or blanket draped over an infant subject in various sleeping postures based on the two processing techniques, as shown in Figure 4. The first technique was for magnifying the motion resulting from chest movement based on 8 level wavelet pyramid decomposition and 5 th order temporal elliptical filtering.