CSI-based human sensing refers to the use of Channel State Information (CSI) extracted from WiFi signals to detect, count, localize, or characterize human presence and activity in an environment without requiring subjects to carry any device or make physical contact with sensors. It matters to the field because it enables passive, privacy-preserving indoor monitoring at low infrastructure cost, with applications ranging from occupancy estimation and people counting to fine-grained multi-person tracking. Key variants include single-zone versus multi-zone occupancy detection, near-field versus far-field sensing configurations, and the distinction between coarse presence detection and finer-grained tasks such as person counting or physical separation of multiple subjects, often addressed through classical signal processing pipelines or deep learning models such as CNN+LSTM architectures.
Source Papers
- A Survey on Wireless Device-free Human Sensing: Application Scenarios, Current Solutions, and Open Issues ↗ — A Survey on Wireless Device-free Human Sensing: Application
- CSI-Based People Counting in WiFi Networks: Leveraging Occupancy Detection ↗ — CSI-Based People Counting in WiFi Networks: Leveraging Occup
- Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey ↗ — Channel State Information from Pure Communication to Sense a
- Deep Learning-Enhanced Human Sensing with Channel State Information: A Survey ↗ — Deep Learning-Enhanced Human Sensing with Channel State Info
- Implementing Wi-Fi CSI-based room-level occupancy Estimation: an experimental study in multi-zone residential environments ↗ — Implementing Wi-Fi CSI-based room-level occupancy Estimation
- MUSE-Fi: Contactless MUti-person SEnsing Exploiting Near-field Wi-Fi Channel Variation ↗ — MUSE-Fi: Contactless MUti-person SEnsing Exploiting Near-fie