CSI-Based WiFi Sensing for Respiration Monitoring and Passive Tracking
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Type
ThesisThesis type
Masters by ResearchAuthor/s
Feng, YifanAbstract
This thesis develops two lightweight and robust WiFi CSI-based frameworks for respiration monitoring and passive localization. For respiration, a dual-branch CNN–Transformer network with attention and contrastive learning discriminates subtle respiratory signals from motion artifacts, ...
See moreThis thesis develops two lightweight and robust WiFi CSI-based frameworks for respiration monitoring and passive localization. For respiration, a dual-branch CNN–Transformer network with attention and contrastive learning discriminates subtle respiratory signals from motion artifacts, achieving over 90% accuracy under mobility and outperforming conventional filtering or frequency-domain methods. For localization, a real-time solution using one transmitter and two receivers employs a CSI quotient model, Doppler features, and differenced AoA/ToF measurements. A geometry-constrained EKF reconstructs sub-meter trajectories, validated across LoS and NLoS scenarios with strong robustness and scalability. Overall, the synergy of deep learning and physical modeling significantly enhances WiFi sensing performance under real-world constraints, demonstrating generalizability on commodity hardware and laying a foundation for smart healthcare and ambient intelligence.
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See moreThis thesis develops two lightweight and robust WiFi CSI-based frameworks for respiration monitoring and passive localization. For respiration, a dual-branch CNN–Transformer network with attention and contrastive learning discriminates subtle respiratory signals from motion artifacts, achieving over 90% accuracy under mobility and outperforming conventional filtering or frequency-domain methods. For localization, a real-time solution using one transmitter and two receivers employs a CSI quotient model, Doppler features, and differenced AoA/ToF measurements. A geometry-constrained EKF reconstructs sub-meter trajectories, validated across LoS and NLoS scenarios with strong robustness and scalability. Overall, the synergy of deep learning and physical modeling significantly enhances WiFi sensing performance under real-world constraints, demonstrating generalizability on commodity hardware and laying a foundation for smart healthcare and ambient intelligence.
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Date
2025Rights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
Faculty of Engineering, School of Electrical and Information EngineeringAwarding institution
The University of SydneyShare