# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Heart Rate Extraction
#### 3.1.3 Photoplethysmography  
Photoplethysmography (PPG) is a simple, low-cost and noninvasive optical bio- monitoring technique used for a few decades to monitor the blood volume changes that occur in the human body vessels due to the blood circulation system. This technology consists of one light emitter and one light detector. With advances in the PPG-based physiological parameter monitoring system, the contact based PPG was extended to a contactless system using images [16]. It is not only applied for heart rate measurement, but also for measuring blood oxygen saturation and respiratory rate [17]. The implementation of such a system is widespread using various types of image capturing devices such as mobile phone cameras, webcams, and thermal cameras [17; 25; 26; 27]. A common way to capture images is using Opto-Physiological Imaging (OPI) with a Complementary Metal-Oxide Semiconductor (CMOS) camera and webcam. Various research directions (scientific camera-based and webcam-based imaging) have been applied to monitor heart rate and blood pressure during rest, exercise, and recovery using PPG. However, only a few studies from the large amount of literature presenting PPG to measure heart rate [28; 29; 30; 31] focus on HR monitoring during exercise [32; 35].  
One of the well-known devices to monitor exercise using PPG technology is the Microsoft Kinect sensor, which can sense the motion using image processing techniques on photoplethysmography images captured by the RGB camera mounted in it. The Kinect RGB camera detects changes in blood flow by mixing PPG signals and volumetric changes in the vessels. For a decade, these devices have been heavily used for monitoring parameters during exercise and rest [18; 19; 32]. [PERSON] [32] proposed a study using the Microsoft Kinects to detect HR at rest and exercise. From the photoplethysmography images captured by the Microsoft Kinect RGB camera, the average color intensity of each color channel of the RGB region of interest (ROI) selected from the forehead was processed through a bandpass digital filter [20], Independent Component Analysis (ICA) [92], and Principle Component Analysis (PCA) [21; 22]. The spectral analysis using Fast Fourier Transform (FFT) of the principal components was applied. The results were compared with a pulse oximeter attached in the body simultaneously to the motion sensing device. A good correlation was found with the oxygen values in the rest situation, but not for the exercise situation, where participants performed rehabilitation exercises.  
Remote PPG have also been proposed to improve heart rate measurement during physical exercise [35]. The main idea behind this version of PPG was to increase the degrees-of-freedom for noise reduction by decomposing the n-wavelength camera signals into multiple orthogonal frequency bands and extracting the pulse-signal per band. The videos of seven subjects were recorded in various practical challenges such as different skin colors of subjects, various light sources, illumination intensities, and exercising modes. The authors suggested that the proposed method increases the robustness of HR measurement in various fitness applications. Other research presented estimations body surface temperature using an Infrared (IR) camera during rest and various stages of exercise (sustained exercise at 50%, 60%, and 70% of age predicted maximum heart rate). HR and Respiratory Rate (RR) were measured with photoplethysmography and motion analysis (vPPG-MA). The results of body surface temperature using IR, and HR and RR using vPPG-MA showed the high level of agreement between the two measurements [15]. [PERSON] et al. [23] presented rPPG based HR estimation during physical exercise by analyzingthe facial images. The authors implemented temporal super pixels and a convolutional neural network. The experiments were carried out on video and were recorded from eleven subjects during intense exercise. The root mean square error was 22.01, which was better than the similar studies.