A real-time WiFi sensing and human activity detection system built using the ESP32-S3 and Channel State Information (CSI).
This project explores how wireless signals can be used for:
- Human presence detection
- Motion sensing
- Activity recognition
- Spatial occupancy estimation
- Real-time CSI visualization
- Edge AI experimentation
The system captures WiFi CSI data directly from the ESP32-S3, processes the signal streams, and visualizes environmental changes caused by human movement and interference.
- ESP32-S3 based CSI data acquisition
- Real-time WiFi packet sniffing
- CSI stream preprocessing and filtering
- Python-based visualization dashboard
- Motion and presence detection pipeline
- Experimental 3D occupancy/heatmap visualization
- Modular firmware architecture using ESP-IDF
- Dataset logging for future ML training
- Research-oriented embedded systems workflow
- ESP32-S3 DevKit M-1 N16R8
- 16MB Flash
- 8MB PSRAM
- ESP-IDF
- FreeRTOS
- C/C++
- Python
- NumPy
- Matplotlib
- PySerial
- Pandas
- VS Code
- Git
- GitHub
This project aims to bridge:
- Embedded Systems
- Wireless Signal Processing
- IoT
- Edge AI
- Smart Environment Sensing
while building a research-grade, portfolio-quality engineering project.
🚧 Phase 1 — ESP32-S3 setup and CSI capture in progress.
- Multi-device CSI synchronization
- Activity classification using ML
- Gesture recognition
- Indoor localization
- TinyML deployment
- Real-time occupancy mapping
- Low-power edge inference
WiFi sensing is an emerging field used in:
- Smart homes
- Healthcare monitoring
- Security systems
- Human-computer interaction
- Robotics
- Next-generation IoT systems
This repository documents the complete engineering journey from low-level firmware development to intelligent sensing applications.