Abstract

Sweating at night can be an indication that there is a disturbance in the human metabolic system. Sweat itself is a substance that is unused in the body or the result of human excretion. The sweat glands are scattered in all parts of the body, but mostly in three locations: armpits, palms, and feet. Several kinds of research related to sweat and Electronic Nose (E-Nose) have also been studied. The study used a patch to absorb sweat and proved the presence of nicotine content from a smoker. However, the previous research has not focused on human sweat at night for potential disease. This paper aims to propose a system to distinguish men and women at night through the armpit sweat odor using Taguchi Gas Sensors (TGS) and SHT15. Researchers found four significant sensors for further investigation: TGS 822, TGS 826, TGS 833, and TGS 2620. This study obtained a total of 165 armpit sweat data, which have been processed and adjusted for this case into 25 data, 12 men (ME) and 13 women (WO). Several classification models are implemented, such as Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT) with accuracy 92.30%, 96.15%, and 84.62%, respectively. Based on the highest accuracy and the Volatile Organic Compounds (VOC) measurement, women are more likely to suffer from several diseases than men, such as leukemia.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE Asia Pacific Conference on Wireless and Mobile, APWiMob 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages121-127
Number of pages7
ISBN (Electronic)9781728194752
DOIs
Publication statusPublished - 8 Apr 2021
Event2021 IEEE Asia Pacific Conference on Wireless and Mobile, APWiMob 2021 - Bandung, Indonesia
Duration: 8 Apr 20219 Apr 2021

Publication series

NameProceedings - 2021 IEEE Asia Pacific Conference on Wireless and Mobile, APWiMob 2021

Conference

Conference2021 IEEE Asia Pacific Conference on Wireless and Mobile, APWiMob 2021
Country/TerritoryIndonesia
CityBandung
Period8/04/219/04/21

Keywords

  • ANNOVA
  • Electronic Nose
  • Gender
  • Machine Learning

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