Development and systematic validation of a system for contactless, camera-based measurement of the heart rate variability

基于摄像头的非接触式心率变异性测量系统的开发和系统验证

• 批准号: 502438143
• 负责人: Professor Dr.-Ing. Ayoub Al-Hamadi
• 金额: --
• 依托单位: Institut für Informations- und Kommunikationstechnik - IIKT
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/502438143?language=en
• 关键词: Development; systematic; validation; system; contactless

Heart rate variability (HRV) provides important information for the medical analysis of the cardiovascular system and the activity of the autonomic nervous system, as well as for the diagnosis and prevention of diseases. Traditional HRV monitoring systems are contact-based techniques that require sensors to be attached directly to the person's body, such as an electrocardiogram (ECG) or contact photoplethysmography (PPG). These techniques are only partially suitable for long-term monitoring or early detection of disease symptoms. In addition, they can have some negative effects on the monitored person, such as skin irritations, an increased risk of spreading disease germs due to direct contact, etc.The aim of this research project is the optical measurement of heart rate variability (HRV) from video images using PPG. PPG is an optical, non-invasive technology that uses light to record volumetric variations of blood circulation in the skin. In recent years, this technique has been realized remotely and contact-free through the use of cameras and has already been successfully used for the measurement of heart rate (HR) from video data. For the measurement of HRV a precise temporal determination of the heartbeat peaks in the PPG signal is necessary. The high measurement accuracy of HR in the state of the art can only be achieved by a strong temporal filtering. However, this makes it impossible to localize the heartbeats precisely over time. A challenge is that even smallest movements and facial expressions of the test persons lead to artifacts in the PPG signal. This is where this research project takes effect, by systematically detecting these artifacts in the PPG signal and subsequently compensating them. Up to now, almost all methods for measuring the PPG signal have been based on color value averaging of (partial) areas of the skin in the face. Movement compensation is not possible with these methods because position informations is lost. To train models that are invariant to movement, deep neural networks (Convolutional Neural Network (CNN)) are well suited. Using 3D head pose estimation methods and action unit recognition (facial muscle movements), a system will be trained to extract motion-invariant PPG signals from video data. For this purpose, information on detected skin regions in each image will be generated using new segmentation methods based on CNN and used for motion compensation. The data obtained by this network will be further processed with another recurrent neural network (Long Short-Term Memory (LSTM)) optimized for temporal signal processing in order to determine the pulse peaks in the PPG signal precisely in time.

心率变异性（HRV）为心血管系统和自主神经系统的活动的医学分析以及疾病的诊断和预防提供了重要信息。传统的HRV监测系统是基于接触的技术，其需要传感器直接附接到人的身体，诸如心电图（ECG）或接触式光电容积描记（PPG）。这些技术仅部分适用于长期监测或早期发现疾病症状。此外，它们可能对被监测的人产生一些负面影响，例如皮肤刺激、由于直接接触而增加的传播病菌的风险等。本研究项目的目的是使用PPG从视频图像中光学测量心率变异性（HRV）。PPG是一种光学、非侵入性技术，其使用光来记录皮肤中血液循环的体积变化。近年来，该技术已通过使用摄像头实现远程和非接触式，并已成功用于从视频数据测量心率（HR）。对于HRV的测量，PPG信号中的心跳峰值的精确时间确定是必要的。在现有技术中，HR的高测量精度只能通过强时间滤波来实现。然而，这使得不可能随时间精确地定位心跳。挑战在于，测试人员的甚至最小的运动和面部表情也会导致PPG信号中的伪影。这就是本研究项目的作用所在，通过系统地检测PPG信号中的这些伪影并随后对其进行补偿。到目前为止，几乎所有用于测量PPG信号的方法都基于面部皮肤的（部分）区域的颜色值平均。这些方法不可能进行运动补偿，因为位置信息会丢失。为了训练对运动不变的模型，深度神经网络（卷积神经网络（CNN））非常适合。使用3D头部姿势估计方法和动作单元识别（面部肌肉运动），将训练系统从视频数据中提取运动不变的PPG信号。为此，将使用基于CNN的新分割方法生成关于每个图像中检测到的皮肤区域的信息，并用于运动补偿。通过该网络获得的数据将使用针对时间信号处理优化的另一个递归神经网络（长短期记忆（LSTM））进一步处理，以便及时精确地确定PPG信号中的脉搏峰值。