ArticleSensors (Basel, Switzerland)2022
Reducing Energy Consumption and Health Hazards of Electric Liquid Mosquito Repellents through TinyML.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed, 5 citations in OpenAlex.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.