# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Heart Rate Extraction
#### 3.1.4 Video-Based Image Processing  
With the rapid advancement in the application of computer vision, researchers have also developed methods for the extraction of reliable heart rate measures [33; 36; 37]. One of them includes the use of face detection and feature extraction to obtain estimates of heart rate. For this, images or videos are recorded using digital cameras that are placed to frame the person's face during exercise performance or at rest. The most used technique to detect face during heart rate estimation is the [PERSON] object detection algorithm. The blind-source separation of the digital signal algorithm [93] to measure heart rate during rest was then purposed by [36]. This study was extended to subsequently measure respiratory rate and HR variability [37].  
Digital image color analysis has also been proposed to help in HR estimation, and several digital filters has been proposed to increase the detection accuracy related to the color channels [38; 39; 40; 41]. A case-study article introduced the use of ICA of each color channel of RGB in the raw images, followed by the application of short-time Fourier Transform (STFT) [41]. Using the proposed methods, root mean square error was less than 2.5 beats per minute (BPM) for heart rate variation between 80 BPM and 130 BPM.  
[PERSON] [42] proposed the advancement of work proposed by [PERSON] [36; 37] using the application of the kNN algorithm after extracting features by ICA. The experiments were carried out in various intensities of exercise performed on stationary bicycles. Other non-contact techniques to measure heart rate include Blood Volume Pulse (BVP), which detects blood volume in cardiovascular dynamics using optical sensors [40]. [PERSON] [25] proposed using facial image processing during exercise to measure BVP. The image pre-processing includes face recognition, band-pass filter [20], trend removal, and reconstruction of source signals to then retrieve BVP. The authors selected only the green channel for the ROI for further signal processing. This signal is applied to ICA and yields three independent signals. A series of image filters, Discrete Time Fourier Transform (DTFT), Discrete Short Time Fourier Transform (Discrete STFT), Discrete Wavelet Transform (DWT), Peak Counting, and Mean Value of Interbit Intervals (IBI) were applied to obtain the frequency spectrum of the raw signal. Both the articles used the image capture in ambient light, straight pose, and neutral emotion.  
The recent development of an exergame connects physical exercise to entertainment, typical of a gaming activity. This could provide solutions to issues regarding treatments of age-related diseases [94]. The proper extraction and implementation of physiological features while using exergames is critical. [PERSON] [34] presented a contactless HR measurement system based on motion-compensated video signals that can be implemented in the remote controllers used for exergames.  
A completely new approach was presented by [PERSON] [24] that measured heart rate and step count during exercise, detected by facial image processing. The authors implemented a chrominance-based adaptive filter and normalization method that enhanced accuracy. The detection rate was 99.52% and 99.7% for stepping and pulse rate using treadmill exercise, respectively. The estimated heart rate was compared to cardiofrequencimeter data during all the activities, showing positive correlation. The authors concluded that step count and heart rate can be measured synchronously during exercise.