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?:abstract
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Fever is a common indicator for symptoms of infections, including SARS-CoV-2 or influenza. Non-contact infrared thermometers are able to measure forehead temperature in a timely manner and perform a fast fever screening in a population. However, forehead temperature measurements differ greatly from basal body temperatures and are the target of massive perturbations from the environment. Here we gathered a dataset of N=19392 measurements using the same precision infrared sensor in different locations while tracking both outside temperature, room temperature, time of measurement, and identity. From this, we propose a method able to extract and remove the influence of external perturbations and set a threshold for fever based on local statistics. This method can help manufacturers and decision-makers to build and use more accurate tools so as to maximize both sensitivity and specificity of the screening protocol.
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?:creator
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?:doi
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?:doi
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10.1101/2020.12.04.20243923
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?:externalLink
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?:license
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?:pdf_json_files
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document_parses/pdf_json/02c73ff80d36cec1d7723cea6d4eadaeca8ecf01.json
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?:publication_isRelatedTo_Disease
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?:sha_id
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?:source
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?:title
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Correction of human forehead temperature variations measured by non-contact infrared thermometer
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?:year
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