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

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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.