We consider articles that were published between January 2018 and April 2021 in the English language. However, some questions are unanswered: (i) Which vital sign is monitored using what type of camera? (ii) What is the performance and which factors affect it? (iii) Which health issues are addressed by camera-based techniques? Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, we conduct a systematic review of continuous camera-based vital sign monitoring using Scopus, PubMed, and the Association for Computing Machinery (ACM) databases. In recent years, noncontact measurements of vital signs using cameras received a great amount of interest. Such datasets would improve comparability and reproducibility in the research field. The paper can sensitize the research community for the problems of video encoding and the proposed recommended practices can help with conducting future experiments and creating valuable datasets that can be shared publicly. We show that increasing the compression rate decreases the accuracy of heart rate estimation, but that both resolution can be reduced (up to a cutoff point) and color subsampling can be applied for reducing file size without a big impact on heart rate estimation.įrom the results, we derive and propose guidelines for recording and encoding of video data for camera based heart rate estimation. To analyze the influence of video compression we compare the effect of several encoding parameters: two modern encoders (H264, H265), compression rate, resolution changes using different scaling algorithms, color subsampling, and file size on two publicly available datasets. In this paper we contribute a comprehensive analysis to answer the question of how to compress video without compromising PPG information. Due to the optimization of modern video codecs for human perception, video compression can influence heart rate estimation negatively by reducing or eliminating small color changes of the skin (PPG) that are needed for camera based heart rate estimation. Because uncompressed video requires huge file sizes, a need for compression algorithms exists to store and share video data. y = src.data īgr.data = clip((298*c + 409*e + 128)/256) īgr.data = clip((298*c - 100*d - 208*e + 128)/256) īgr.Public databases are important for evaluating and comparing different methods and algorithms for camera based heart rate estimation. Tried doing the YUV444 conversion but it just made my image appear in shades of green. I see that I should be converting from YUV444 to RGB now but I don't quite I understand what the clip function does in the sample I found on Wiki. The problem is my image does not return to its original colours. ![]() ![]() I'm using OpenCV so I tried to interpret this as best I can: y = src.data I then used this formula, from Wikipedia, to access the Y, U, and V planes: size.total = size.width * size.height ![]() I think I did this correctly, I took 2x2 submatrices from the YUV matrix, ordered the values from least to greatest, and took the average between the 2 values in the middle. I then did a 4:2:0 chroma subsample on the matrix.
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