COVIDGuardian: A Machine Learning approach for detecting the Three Cs

Kento Katsumata, Yuka Honda, Tadashi Okoshi, Jin Nakazawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution


On January 30, 2020, WHO officially declared the outbreak of COVID-19 a Public Health Emergency of International Concern. Japan announced the state of emergency and implemented safety protocols the "Three Cs", a warning guideline addressing to voluntarily avoid potentially COVID-19 hazardous situations such as confined and closed spaces, crowded places and close-contact settings that lead to occurrence of serious clusters. The primary goal of this research is to identify the factors which help to estimate whether the user is in the Three Cs. We propose COVIDGuardian, a system that detects the Three Cs based on data such as CO2, temperature, humidity, and wireless packet log. The results show that estimation of closed space had the highest accuracy followed by close-contact settings and crowded places. The ensemble Random Forest (RF) classifier demonstrates the highest accuracy and F score in detecting closed spaces and crowded spaces. The findings indicated that integrated loudness value, average CO2, average humidity, probe request log, and average RSSI are of critical importance. In addition, when the probe request logs were filtered at three RSSI cutoff points (1m, 3m, and 5m), 1m cut-off points had the highest accuracy and F Score among the Three C models.

Original languageEnglish
Title of host publicationIoT 2022 - Proceedings of the 12th International Conference on the Internet of Things 2022
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450396653
Publication statusPublished - 2022 Nov 7
Event12th International Conference on the Internet of Things, IoT 2022 - Delft, Netherlands
Duration: 2022 Nov 72022 Nov 10

Publication series

NameACM International Conference Proceeding Series


Conference12th International Conference on the Internet of Things, IoT 2022


  • COVID-19
  • Context Awareness
  • Machine Learning

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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