I. BACKGROUND ON TRADITIONAL ACTIVITY RECOGNITION SYSTEMS
A. Techniques Based on Wi-Fi Signal Power
B. Techniques Requiring Modified WiFi Hardware

II. WIFI CHANNEL STATE INFORMATION
A. CSI of WiFi System
B. Limitations and Errors of WiFi Systems
C. Effect of Human Motion on Wireless Channel

III. WI-FI CSI-BASED BEHAVIOUR RECOGNITION
A. Histogram-based Techniques
B. CARM
C. Using Deep Learning for Behaviour Recognition

IV. EVALUATION OF DIFFERENT METHODS
A. Measurement Setup
B. Evaluating Machine Learning Techniques

V. DISCUSSIONS
A. Effect of Environment Change on the Performance
B. Effect of Wi-Fi transmission rate on the performance
C. Using CSI Phase Information
D. Multi-user Activity Recognition

VI. CONCLUSION AND FUTURE WORK