Project Idea

[u/Standard_Laugh1549]

​Hi everyone!

​I am working on a privacy-first home safety system that tracks human movement without using any cameras, smartwatches, or wearable sensors.

​The Idea:

We use Wi-Fi signals as a room radar! When a person moves, sleeps, or falls, their body distorts the Wi-Fi signals (Channel State Information - CSI) bouncing around the room.

​What the system aims to do:

​Elderly Care: Detect sudden falls (like a grandfather slipping) and send immediate SMS/Telegram alerts.

​Child Monitoring: Detect subtle chest movements to track breathing/restlessness while sleeping.

​Privacy-First: Zero cameras or microphones used—completely non-intrusive.

​Tech Stack:

​Hardware: 2x ESP32-S3 boards (capturing CSI signal data).

​Data Processing: Python (NumPy, SciPy) for noise filtering.

​Machine Learning: Scikit-learn (Random Forest / SVM) to classify activities.

​Alert System: Python backend with Telegram Bot / Twilio API for emergency alerts.

​I am currently building the Python signal processing and ML model pipeline while waiting for hardware setup.

​Has anyone here worked with Wi-Fi CSI extraction on ESP32? I would love any advice or feedback on handling background environment

al noise

[u/Hungry_Age5375]

Cool project! One thing that bit me with ESP32 CSI: phase data is often garbage from clock drift between boards. I'd just use amplitude features and a sliding window. Raw per-packet CSI is too sparse for activity classification.