Three Cs detection method using Wi-Fi radio wave statistics.

Area of Science:

• Infection Prevention and Control
• Network Engineering
• Public Health

Background:

• The COVID-19 pandemic highlighted the need for infection prevention measures, particularly avoiding "Three Cs": closed spaces, crowded places, and close-contact settings.
• Existing detection systems for "Three Cs" often rely on costly equipment like cameras and CO2 sensors.
• The difficulty for individuals to consistently monitor and avoid "Three Cs" necessitates automated solutions.

Purpose of the Study:

• To develop a cost-effective method for detecting "Three Cs" environments.
• To leverage existing Wi-Fi infrastructure for real-time situational awareness.
• To provide an accessible tool for businesses and public spaces to manage infection risks.

Main Methods:

• Utilized existing Wi-Fi equipment to detect "Three Cs" situations.
• Introduced unique detection parameters for analyzing Wi-Fi signals.
• Performed statistical evaluations to assess the effectiveness of the detection method.

Main Results:

• Developed and evaluated a system for detecting "Three Cs" using Wi-Fi.
• Achieved a detection accuracy of 86% or higher.
• Demonstrated the feasibility of using ubiquitous Wi-Fi for infection risk assessment.

Conclusions:

• A Wi-Fi-based system can effectively detect "Three Cs" environments with high accuracy.
• This approach offers a cost-efficient alternative to traditional sensor-based systems.
• The proposed method supports infection prevention strategies by identifying high-risk settings.