Contactless Small-Scale Movement Monitoring System Using Software Defined Radio for Early Diagnosis of COVID-19.

Area of Science:

• Wireless Sensing
• Biomedical Engineering
• Public Health Technology

Background:

• The COVID-19 pandemic necessitates contactless health monitoring and movement tracking.
• Existing methods for symptom detection and movement monitoring are often limited in a pandemic context.
• There is a need for innovative platforms to address these challenges.

Purpose of the Study:

• To propose a novel contactless sensing platform for detecting COVID-19 symptoms and monitoring human movements.
• To leverage Software Defined Radio (SDR) technology for real-time health and movement analysis.
• To provide a flexible and multifunctional solution for pandemic response.

Main Methods:

• Utilized Software Defined Radio (SDR) technology to capture Channel Frequency Response (CFR) changes.
• Analyzed Orthogonal Frequency Division Multiplexing (OFDM) subcarrier variations caused by human motion.
• Employed zero-cross detection, peak detection, and Fourier transformation to analyze breathing patterns.

Main Results:

• Successfully detected hand movement, coughing, and breathing.
• Accurately captured normal (20 bpm), slow (10 bpm), and fast (28 bpm) breathing rates.
• Demonstrated the platform's capability to record breathing rates using multiple analytical methods.

Conclusions:

• The proposed SDR-based platform offers a portable and flexible solution for contactless health monitoring.
• The system can detect respiratory abnormalities and monitor human movement.
• Future work can involve AI-driven classification for broader health condition detection.