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• W4295537332 abstract "The current technological advances have pushed the quantification of exercise intensity to new era of physical exercise sciences. Monitoring physical exercise is essential in the process of planning, applying, and controlling loads for performance optimization and health. A lot of research studies applied various statistical approaches to estimate various physiological indices, to our knowledge, no studies found to investigate the relationship of facial color changes and increased exercise intensity. The aim of this study was to develop a non-contact method based on computer vision to determine the heart rate and, ultimately, the exercise intensity. The method was based on analyzing facial color changes during exercise by using RGB, HSV, YCbCr, Lab, and YUV color models. Nine university students participated in the study (mean age = 26.88 ± 6.01 years, mean weight = 72.56 ± 14.27 kg, mean height = 172.88 ± 12.04 cm, six males and three females, and all white Caucasian). The data analyses were carried out separately for each participant (personalized model) as well as all the participants at a time (universal model). The multiple auto regressions, and a multiple polynomial regression model were designed to predict maximum heart rate percentage (maxHR%) from each color models. The results were analyzed and evaluated using Root Mean Square Error (RMSE), F-values, and R-square. The multiple polynomial regression using all participants exhibits the best accuracy with RMSE of 6.75 (R-square = 0.78). Exercise prescription and monitoring can benefit from the use of these methods, for example, to optimize the process of online monitoring, without having the need to use any other instrumentation." @default.
• W4295537332 created "2022-09-14" @default.
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• W4295537332 date "2022-09-09" @default.
• W4295537332 modified "2023-09-30" @default.
• W4295537332 title "Using Computer Vision to Track Facial Color Changes and Predict Heart Rate" @default.
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• W4295537332 doi "https://doi.org/10.3390/jimaging8090245" @default.
• W4295537332 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36135410" @default.
• W4295537332 hasPublicationYear "2022" @default.
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