Non-contact Monitoring of Breathing Function Using Infrared Imaging

We have developed a novel method for non-contact measurement of breathing function. The method is based on statistical modeling of dynamic thermal data captured through a highly sensitive infrared imaging system. The air that is breathed out has higher temperature than the typical background of indoor environments (e.g., walls). Therefore, the particles of the expired air emit at a higher power than the background, a phenomenon that is captured as a distinct thermal signature in the infrared imagery. There is significant technical difficulty in computing this signature, however, because the phenomenon is of very low intensity and transient nature. We use an advanced statistical algorithm based on multi-Normal data representation, the method of moments, and the Jeffreys divergence measure to address the problem. In experimental tests, we were able to compute correctly the breathing waveforms in eight (8) infrared video clips of three (3) subjects at distances ranging from 6-8 feet. The results were compared with ground-truth data collected concomitantly with a traditional contact sensor. Our experiments demonstrated the promise of this modality, which may find applications among others in the next generation contact-free polygraphy and in sleep studies. Index Terms Breathing function, thermal imaging, Jeffrey’s divergence measure, method of moments, contact-free polygraphy, sleep studies