Aberystwyth University, Aberystwyth, UK.
Adv Exp Med Biol. 2021;1317:181-202. doi: 10.1007/978-3-030-61125-5_10.
In this chapter, we review methods for video-based heart monitoring, from classical signal processing approaches to modern deep learning methods. In addition, we propose a new method for learning an optimal filter that can overcome many of the problems that can affect classical approaches, such as light reflection and subject's movements, at a fraction of the training cost of deep learning approaches. Following the usual procedures for region of interest extraction and tracking, robust skin color estimation and signal pre-processing, we introduce a least-squares error optimal filter, learnt using an established training dataset to estimate the photoplethysmographic (PPG) signal more accurately from the measured color changes over time. This method not only improves the accuracy of heart rate measurement but also resulted in the extraction of a cleaner pulse signal, which could be integrated into many other useful applications such as human biometric recognition or recognition of emotional state. The method was tested on the DEAP dataset and showed improved performance over the best previous classical method on that dataset. The results obtained show that our proposed contact-free heart rate measurement method has significantly improved on existing methods.
在本章中，我们回顾了基于视频的心脏监测方法，包括经典的信号处理方法和现代的深度学习方法。此外，我们提出了一种新的学习最优滤波器的方法，该方法可以克服许多可能影响经典方法的问题，例如光反射和对象的运动，而其训练成本仅为深度学习方法的一小部分。在进行感兴趣区域提取和跟踪、稳健的肤色估计和信号预处理之后，我们引入了一种最小二乘误差最优滤波器，该滤波器使用经过验证的训练数据集来学习，以便更准确地从随时间变化的测量颜色变化中估算光电容积脉搏波（PPG）信号。该方法不仅提高了心率测量的准确性，而且还提取出了更干净的脉搏信号，该信号可集成到许多其他有用的应用程序中，例如人体生物识别或情感状态识别。该方法在 DEAP 数据集上进行了测试，在该数据集上的表现优于之前最好的经典方法。所得结果表明，我们提出的非接触式心率测量方法在现有方法的基础上有了显著的改进。
