Respiratory rate is one of the most important but underutilized vital signs. Modern medical practices have standard automated methods to detect abnormalities in heart rate, blood oxygen concentration, temperature, and blood pressure, but not for respiratory rate. Due to the time-consuming, patient-contact intensive observations that are required to measure respiratory rate is tedious to do in a hospital setting.

This paper proposes a method to measure respiratory rate using a convolutional neural network in thermal imaging. Twenty subjects were used to collect 38,394 images of inhaling and exhaling used for training the convolutional neural network. The convolutional neural network has proved to be highly accurate when tested on ten different subjects, each in three distinct positions. In the 30 validation recordings, 100% of respiratory rate values were within ±2 breaths of the manually counted value. If the performance is measured at ±1 breath, then the accuracy of this respiratory rate detector is 86.6% which is more accurate than manual counting. It has also been demonstrated that subjects at slight angles (10° - 45°) between the camera and the subject’s midsagittal plane provide a greater number of exact breath counts.