Device-free sensing refers to the use of wireless radio frequency signals — such as WiFi or Bluetooth — to detect, count, or monitor people and activities in an environment without requiring individuals to carry or wear any dedicated hardware or tracking device. This approach matters significantly for the field because it enables passive, unobtrusive monitoring for applications such as indoor occupancy detection and crowd counting, lowering barriers to deployment in real-world settings where user participation cannot be assumed. Key variants include binary occupancy detection (presence versus absence), people counting, and activity recognition, each differing in the granularity of inference drawn from signal perturbations such as channel state information (CSI) amplitude and phase changes caused by human body movement or passive fidgeting.
Source Papers
- A Framework to Estimate Classroom Occupancy using WiFi Channel State Information ↗ — A Framework to Estimate Classroom Occupancy using WiFi Chann
- A survey on CSI-based Wi-Fi sensing datasets and models with a focus on reproducibility ↗ — A survey on CSI-based Wi-Fi sensing datasets and models with
- An Overview on IEEE 802.11bf: WLAN Sensing ↗ — An Overview on IEEE 802.11bf: WLAN Sensing
- BLE Can See: A Reinforcement Learning Approach for RF-based Indoor Occupancy Detection ↗ — BLE Can See: A Reinforcement Learning Approach for RF-based
- CSI-Based NTC Using Ambient WiFi: Channel Selection, Topology Control and Traffic Interference ↗ — CSI-Based NTC Using Ambient WiFi: Channel Selection, Topolog
- CSI-Chain: A Complete End-to-End Framework for WiFi CSI Sensing ↗ — CSI-Chain: A Complete End-to-End Framework for WiFi CSI Sens
- Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey ↗ — Channel State Information from Pure Communication to Sense a
- DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-Shot Learning ↗ — DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor
- Deep Learning-Enhanced Human Sensing with Channel State Information: A Survey ↗ — Deep Learning-Enhanced Human Sensing with Channel State Info
- Device-Free Passive Identity Identification via WiFi Signals ↗ — Device-Free Passive Identity Identification via WiFi Signals
- Device-free occupancy detection and crowd counting in smart buildings with WiFi-enabled IoT ↗ — Device-free occupancy detection and crowd counting in smart
- Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations ↗ — Exposing the CSI: A Systematic Investigation of CSI-based Wi
- Fast and Robust Stationary Crowd Counting With Commodity WiFi ↗ — Fast and Robust Stationary Crowd Counting With Commodity WiF
- Guiding Wi-Fi Sensor Placement for Enhanced CSI-Based Sensing in Stationary Crowd Counting ↗ — Guiding Wi-Fi Sensor Placement for Enhanced CSI-Based Sensin
- Human Activity Recognition via Wi-Fi and Inertial Sensors With Machine Learning ↗ — Human Activity Recognition via Wi-Fi and Inertial Sensors Wi
- Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond ↗ — Integrated Sensing and Communications: Toward Dual-Functiona
- Investigation of Environment Dependence in Wi-Fi CSI-Based Crowd Counting Systems ↗ — Investigation of Environment Dependence in Wi-Fi CSI-Based C
- MMCOUNT: Stationary Crowd Counting System Based on Commodity Millimeter-Wave Radar ↗ — MMCOUNT: Stationary Crowd Counting System Based on Commodity
- OPERAnet, a multimodal activity recognition dataset acquired from radio frequency and vision-based sensors ↗ — OPERAnet, a multimodal activity recognition dataset acquired
- Passive WiFi Radar for Human Sensing Using a Stand-Alone Access Point ↗ — Passive WiFi Radar for Human Sensing Using a Stand-Alone Acc
- Towards Environment Independent Device Free Human Activity Recognition ↗ — Towards Environment Independent Device Free Human Activity R
- Understanding and Modeling of WiFi Signal Based Human Activity Recognition ↗ — Understanding and Modeling of WiFi Signal Based Human Activi
- WiFi Sensing with Channel State Information ↗ — WiFi Sensing with Channel State Information
- WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing ↗ — WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activi