Comparison of motion-based analysis to thermal-based analysis of thermal video in the extraction of respiration patterns.

Non-contact methods of extracting vital signals has become a popular area of research. This is likely due to the world's aging population and the increased need for long term and remote monitoring. This paper examines and compares the potential for one modality to capture a vital sign, specifically respiration, in the presence of signal abnormalities. This paper compares temperature based-methods to motion-based methods of extracting respiration rate from thermal video of a subject performing computationally difficult respiration tests. The thermal video was subjected to segmentation-based image processing and region tracking to encompass temperature changes over time. All methods were successful in identifying regular breathing and the absence of breathing, but differed in performance identifying hyperventilation and obstructive sleep apnea simulated breathing. The temperature-based method better depicted airflow volume, while the motion-based method better depicted absence of breath and chest movement; neither signal on its own was able to accurately depict OSA breathing. These results suggest that the fusion of information from different physical phenomenon (i.e. motion and temperature) is important here in detecting abnormal breathing patterns, but also in the detection of all vital signals, adding algorithmic robustness in the presence of signal abnormalities.