![]() ![]() Under ideal conditions, the root mean squared error is around 2.60 bpm, 2.22 cpm, 6.91 mm Hg, 4.88 mm Hg, and 0.86 ☌ for HR, RR, systolic BP, diastolic BP, and BST, respectively. So far, BP and SpO2 are monitored with RGB cameras only, whereas BST is derived from FIR cameras only. HR and RR can be measured using red, green, and blue (RGB) and near-infrared (NIR) as well as far-infrared (FIR) cameras. ![]() One hundred and four articles remained: 60, 20, 6, 2, and 1 of the articles focus on HR, RR, BP, BST, and SpO2, respectively, and 15 on multiple vital signs. ![]() In total, we retrieve 905 articles and screened them regarding title, abstract, and full text. We include five vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), body skin temperature (BST), and oxygen saturation (SpO2). 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. ![]() But it can still perform good quality after decoding.ġ.planer: each Y, U and V put separately in the momoryĢ.semi-planer:Y and UV put separately in memory, the difference between planer and semi-planer is that UV format put together in semi-planer.Public databases are important for evaluating and comparing different methods and algorithms for camera based heart rate estimation. YUV format usually has smaller bandwidth than RGB format, because it reduced the chrominance information. The Y channel is for luminance value and U&V channels are for color value. The concept of YUV format is to separate the color information and luminance information. YUV format is a color encoded system, it can be transformed by RGB format. ![]()
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