Improving Infrared Sensor Temperature Readings by Machine Analysis of Emissivity

Abstract

Temperature is an important and first step in determining the health of individuals. Using an infrared temperature sensor is easy to do, quick, and does not involve touching a patient. Current devices are useful but technological advances in electronics have brought new capabilities to infrared temperature readings. One advance has narrowed the field of view and thereby increased the distance range of infrared thermometers. This feature would allow health care personnel to avoid exposure to a contagious zone surrounding a patient. A second feature allows users to include the emissivity of infrared readings for humans. Neither of these advances can be exploited by current infrared thermometers leaving a void in the practical application of this new breed of infrared sensors. A 2014 report by the Canadian Agency for Drugs and in Health (CADTH) questioned the accuracy of infrared thermometers and called for more research.

This dissertation explores the parameters of the basic physics underlying infrared sensors. A methodology is developed to conduct various testing regimes using C++ or Python software programming and two surveys of students were conducted using a modern sensor. The evaluation of the results determined the accuracy and range of infrared sensor temperature readings were improved with the inclusion of the emissivity parameter using machine analysis of emissivity.