Reducing Energy Consumption and Health Hazards of Electric Liquid Mosquito Repellents through TinyML

Inyeop Choi, Hyogon Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Two problems arise when using commercially available electric liquid mosquito repellents. First, prallethrine, the main component of the liquid repellent, can have an adverse effect on the human body with extended exposure. Second, electricity is wasted when no mosquitoes are present. To solve these problems, a TinyML-oriented mosquito sound classification model is developed and integrated with a commercial electric liquid repellent device. Based on a convolutional neural network (CNN), the classification model can control the prallethrine vaporizer to turn on only when there are mosquitoes. As a consequence, the repellent user can avoid inhaling unnecessarily large amounts of the chemical, with the added benefit of dramatically reduced energy consumption by the repellent device.

Original languageEnglish
Article number6421
JournalSensors
Volume22
Issue number17
DOIs
Publication statusPublished - 2022 Sep

Keywords

  • TinyML
  • convolutional neural network (CNN)
  • electric liquid mosquito repellent
  • embedded AI
  • energy saving
  • health
  • prallethrine

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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