Doppler shift refers to the change in frequency of a received WiFi signal caused by the relative motion between a transmitter, receiver, and any reflecting objects in the environment, such as a moving human body. In CSI-based sensing, Doppler shift analysis is a foundational method for detecting and characterizing motion, enabling the inference of activities such as walking, gesturing, and breathing by measuring how reflected signal components are frequency-shifted relative to the static channel response. Key variants include micro-Doppler signatures, which capture fine-grained frequency modulations produced by subtle or periodic movements like limb articulation or chest wall displacement during respiration, and Doppler velocity profiles, which encode speed and direction information used to distinguish between different activity classes.
Source Papers
- A Survey on Human Behavior Recognition Using Channel State Information ↗ — A Survey on Human Behavior Recognition Using Channel State I
- An Overview on IEEE 802.11bf: WLAN Sensing ↗ — An Overview on IEEE 802.11bf: WLAN Sensing
- 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
- Doppler Effect: Analyses and Applications in Wireless Sensing and Communications ↗ — Doppler Effect: Analyses and Applications in Wireless Sensin
- Efficient machine learning for Wi-Fi CSI-based human activity recognition using fast Monte Carlo based feature extraction ↗ — Efficient machine learning for Wi-Fi CSI-based human activit
- Human Activity Recognition via Wi-Fi and Inertial Sensors With Machine Learning ↗ — Human Activity Recognition via Wi-Fi and Inertial Sensors Wi
- 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
- Time matters: Empirical insights into the limits and challenges of temporal generalization in CSI-based Wi-Fi sensing ↗ — Time matters: Empirical insights into the limits and challen
- 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
- WiGNN: WiFi-Based Cross-Domain Gesture Recognition Inspired by Dynamic Topology Structure ↗ — WiGNN: WiFi-Based Cross-Domain Gesture Recognition Inspired