## 3 Different Aspects of Colour-Based Method
### Motion Artefacts
Additionally, they considered the second component produced by ICA to be the PPG signal in every case. In [74], a motion compensated method was designed based on single-channel independent component analysis (SCICA) to extract both HR and RR using a CMOS video camera during exercise. Moreover, they implemented image stabilisation via 2D cross correlation and used time-frequency representation (TFR) for spectral analysis. Still, the motion artefacts could not be totally removed using this method. Moreover, in the extracted components, physiological waveforms were different under different situations. [PERSON] et al. [75] introduced an improved motion tolerant technique to measure both average and instantaneous HR via webcam, considering a substantial amount of movement by the body as well as the head. After detecting a face by the V-J method, they used the speeded up robust feature (SURF) detection algorithm to find trackable interest points in the facial region and exploited the Kanade-Lucas-Tomasi (KLT) algorithm for tracking the faces. To extract the signal, they used an adaptive bandpass filter and automatic sorting algorithm to sort ICA output components by taking a sine function as a reference signal. Nevertheless, this method is not suitable if the subject walks or engages in other difficult movements and the range tested was also very short, with limited ROI options. Using a digital camera to remotely measure heart rate, [PERSON] et al. [76] proposed a new technique to improve the photoplethysmography signal by combining facial sub-region landmark localisation and joint blind source separation (JBSS) methods to extract the physiological signal. However, blind source separation-based approaches may have limitations when exposed to other incidental periodic signals, including illuminance or motion variations. To overcome this limitation, some researchers have considered different techniques to suppress motion artefacts rather than using BSS. For instance, a continuous wavelet transform-based method was presented in [77] to extract both instantaneous heart and respiratory rates using a webcam with normal head movements. However, their motion consideration was limited to head movements and they did not consider illumination variation or other movements. [PERSON] et al. [78] proposed a robust technique to extract HR from CCD camera video based on chrominance (CHROM) during exercise. In chrominance-based methods, the colour difference signals are combined linearly considering a standardized skin tone. However, in this case they considered two fitness devices, a stationary bike and a stepping device, with only one subject. Their method was also affected by skin colour, especially dark skin. This method was further improved in [79] by using the blood volume pulse vector and five different exercise devices. Here, blood volume pulse (BVP) was considered as a signature for various reflection spectra of skin for explicitly distinguishing the physiological signal from noise caused by motion. Another study by [PERSON] et al. [80] presented a system, robust to motion, using adaptive colour variation between red and green channels as well as an adaptive bandpass filter (ABF) to measure HR from moving subjects,exploiting a webcam. Using a CCD camera, [PERSON] et al. [81] introduced a new robust technique exploiting spatial subspace rotation (2 SR), where a spatial subspace of skin-pixels was estimated and its temporal rotation was measured in the image domain to measure HR. Nevertheless, as 2 SR is a completely data-driven algorithm, it might cause undetected inaccurate results because of noise or a poorly selected skin-mask. Another algorithm to measure HR by exploiting imaging of the plane orthogonal to skin (POS) was presented in [67] where normalized RGB channels were combined into two new channels which were merged by weighting to the desired signal. Later, to allow the independent reduction of various motion frequencies by exploiting sub band (SB) decomposition, [PERSON] et al.