Towards Autonomous Physiological Signal Extraction From Thermal Videos Using Deep Learning

Abstract

Using the thermal modality in order to extract physiological signals as a noncontact means of remote monitoring is gaining traction in applications, such as healthcare monitoring. However, existing methods rely heavily on traditional tracking and mostly unsupervised signal processing methods, which could be affected significantly by noise and subjects’ movements. Using a novel deep learning architecture based on convolutional long short-term memory networks on a diverse dataset of 36 subjects, we present a personalized approach to extract multimodal signals, including the heart rate, respiration rate, and body temperature from thermal videos. We perform multimodal signal extraction for subjects in states of both active speaking and silence, requiring no parameter tuning in an end-to-end deep learning approach with automatic feature extraction. We experiment with different data sampling methods for training our deep learning models, as well as different network designs. Our results indicate the effectiveness and improved efficiency of the proposed models reaching more than 90% accuracy based on the availability of proper training data for each subject.