COVIDGuardian: A Machine Learning approach for detecting the Three Cs

Kento Katsumata, Yuka Honda, Tadashi Okoshi, Jin Nakazawa

研究成果: Conference 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.

ホスト出版物のタイトルIoT 2022 - Proceedings of the 12th International Conference on the Internet of Things 2022
出版社Association for Computing Machinery
出版ステータスPublished - 2022 11月 7
イベント12th International Conference on the Internet of Things, IoT 2022 - Delft, Netherlands
継続期間: 2022 11月 72022 11月 10


名前ACM International Conference Proceeding Series


Conference12th International Conference on the Internet of Things, IoT 2022

ASJC Scopus subject areas

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ ネットワークおよび通信


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