An Approach to Non-contact Monitoring of Respiratory Rate and Breathing Pattern Based on Slow Motion Images

Abstract

Respiratory rate is the first observation to indicate a health problem. This study presents an approach to noncontact monitoring of respiratory rate and breathing pattern based on slow-motion images focus on sleeping positions. The movement while breathing is too tiny to be observed with the naked eyes. The body movement is captured by the slow-motion mode built in a smartphone camera. The primary benefit of this approach is the utilization of an accessibility device which everyone can use at home. The respiratory rate was obtained from the intensity value in the selected region of interest around the chest and abdomen area with used the Gaussian filter to reduce the noise. A motion tracking algorithm was implemented to track the region of interest movements. The obtained signal should be smoothed to reflect the breathing pattern then the Findpeaks function is applied in order to count the number of peaks for representing the number of the breaths. The result demonstrates that simple computer vision techniques can provide highly accurate breathing assessment. The accuracy depends on the location and size of region of interest, signal smoothing, and filter types. Besides, other variables affect accuracy, such as background views or patterns on clothing.