Why the study?
Interest in contactless heart rate measurement is growing, and deep learning methods have recently been used to improve conventional performance, prompting a review and direct comparison.
Which publicly available deep learning method provides the most accurate contactless heart rate measurement on the UBFC dataset?
Which publicly available deep learning method provides the most accurate contactless heart rate measurement on the UBFC dataset?
The deep learning method PhysNet provides highly accurate contactless heart rate measurement compared to other publicly available methods.
Answers are grounded in this paper and cite their sources.
Comparison of available deep learning methods benchmarks contactless heart rate approaches; leaves open prospective validation before clinical use.
The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.
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Ni et al. (2021) studied this question.
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