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Miniaturization and expansion of the contactless temperature measurement system. Facial temperatures in relation to age, pulse and gender.

Authors: Fabian, Sylwester; Spinczyk, Dominik; Kopciowski, Dariusz; Fabian, Aleksandra;

Miniaturization and expansion of the contactless temperature measurement system. Facial temperatures in relation to age, pulse and gender.

Abstract

The dataset contains temperature measurements on the surface of the face taken on 109 people. Each patient (identified by Patient ID in dataset) acclimatized in a room with a temperature of 22-24 degrees Celsius. Then the person completed a survey, during which they provided their: age (column Survey - age [years]), gender (column Survey - Gender), temperature measurement using a pyrometer thermometer (column Survey - temperature [°C]), and pulse measurement using a pulse oximeter (column Survey - measured pulse [BPM]). After that, the examined person stood in front of the contactless temperature measurement system (using a thermal camera), which was continuously calibrated to the black body at a distance of 1.5-3 meters (column Distance between camera and patient [m]). Then, several hundred temperature measurements were taken on each person in the following ways: Median temperature on face [°C] Median temperature on face, 1% of pixels with max temperature [°C] Median temperature on face, 5% of pixels with max temperature [°C] Median temperature on face, 10% of pixels with max temperature [°C] Median temperature in the center of the eyes (3x3 pixels) [°C] Median temperature measured at the corners of the eyes (3x3 pixels) [°C] Additionally, the system automatically estimated: the age of the examined person (column Estimated Age [years]), the pulse of the examined person (column Estimated Pulse [BPM]), and gender (Estimated Gender). According to [1], the measured temperature on the surface of the face is influenced by the age of the measured person. As part of the project, a Binary Regression Tree was developed, which considers (estimated) age when calculating the temperature on the surface of the face (column Temperature calculated by Binary Tree Regression algorithm [°C]). [1] Cheung, Ming & Chan, Lung & Lauder, I & Kumana, Cyrus. (2012). Detection of body temperature with infrared thermography: accuracy in detection of fever. Hong Kong medical journal = Xianggang yi xue za zhi / Hong Kong Academy of Medicine. 18 Suppl 3. 31-4.

The dataset was created as part of the project with number POIR.01.01.01-00-1215/20-00 funded by the Polish National Center for Research and Development (NCBiR).

Keywords

Fever, Computer and Information Science, Medicine, Health and Life Sciences, Temperature, COVID-19, Temperature, COVID 19, Fever detection, Fever Detection

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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