Why the study?
Traditional heart rate monitoring methods are difficult to use outside clinical settings due to lack of portability and connection problems, prompting contactless computer vision approaches.
A novel video magnification technique using the Hermite transform and deep learning improves the accuracy of contactless beat-by-beat heart rate estimation compared to traditional Gaussian pyramid methods.
New contactless pulse detection via video magnification; leaves open clinical validation before practice adoption.
Monitoring of heart rate can be used in many medical and sports applications. Lack of portability and connection problems make traditional monitoring methods difficult to use outside of clinical environments. The computer vision techniques have shown that some physiological variables as heart rate can be measured without contact. Video magnification is one of these approaches used for the detection of the pulse signal. In this paper we propose a new strategy to magnify motion in a video sequence using the Hermite transform. In addition, a deep learning technique is implemented to estimate the beat-by-beat pulse signal. We trained the system and validated our results using an electronic pulse monitoring device. Our approach is compared with the classical video magnification using a Gaussian pyramid. The results show a better enhancement of spectral information from the color changes allowing an accurate estimation of the instantaneous beat by beat pulse than the Gaussian approach.
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M. Jaya Joshna (2023) studied this question.
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