Moya-Albor Ernesto, Brieva Jorge, Ponce Hiram, Rivas-Scott Orlando, Gomez-Pena Cristina
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2595-2598. doi: 10.1109/EMBC.2018.8512879.
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 been shown that some physiological variables as heart rate can be measured without contact. Video magnification is one of these approach 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 colour changes allowing an accurate estimation of the instantaneous beat by beat pulse than the Gaussian approach.
心率监测可应用于许多医学和体育领域。传统监测方法缺乏便携性和连接问题，使得其在临床环境之外难以使用。计算机视觉技术已表明，诸如心率等一些生理变量可以非接触方式测量。视频放大是用于检测脉搏信号的方法之一。在本文中，我们提出了一种使用埃尔米特变换来放大视频序列中运动的新策略。此外，还实施了一种深度学习技术来逐拍估计脉搏信号。我们使用电子脉搏监测设备对系统进行了训练并验证了结果。我们的方法与使用高斯金字塔的经典视频放大方法进行了比较。结果表明，与高斯方法相比，我们的方法能更好地增强来自颜色变化的光谱信息，从而能够准确估计瞬时逐拍脉搏。
