Inspiration

The LA wildfires opened our eyes to a serious challenge facing firefighters. 77% of firefighter fatalities occur from disorientation during rapid breaches of enclosed structures.. When these first responders enter smoke-filled buildings, they often can’t see more than a few feet ahead – making an already dangerous job even riskier. That’s what inspired us to create Theia, a technology that lets firefighters detect people through walls, even in zero-visibility conditions. By giving them this “sixth sense,” we’re helping ensure both firefighters and the people they’re trying to save make it home safely. The project name “Theia” was named after the Greek goddess of sight and vision.

The Vision

Our aim was to develop an extremely low-cost, real-time human detection system that utilizes commodity ESP32 microcontrollers & cutting-edge AI to provide firefighters with “through-wall” vision via a mixed-reality headset.
Imagine navigating a burning building, then seeing which walls have trapped individuals behind them, & which walls are in front of empty rooms — all overlaid onto your vision. That’s Theia.

How It Works: From ESP32 to the Mixed Reality headset

Our first task was to interpret Channel State Information (CSI) data, the fingerprint of WiFi signals. We transformed inexpensive ESP32s into a rudimentary radio-frequency sensing system:

• WiFi Radar (ESP32s): Two ESP32s act as a radar system. One (CSI-TX) transmits WiFi packets, and the other (CSI-RX) captures the resulting CSI data, containing unique signal reflections caused by objects, (or humans) even through walls. We pushed the ESP32 CSI Tool framework to its limits, optimizing sampling configurations for real-time data.

• Real-time Data Pipeline: We implemented a custom data pipeline to stream the raw CSI data from the ESP32 to an NVIDIA Jetson Nano (2GB). The system was configured to capture CSI measurements across 30 subcarriers at a sampling rate of 100 Hz (10ms intervals), with each capture window containing 50 packets. While this configuration allowed for detailed channel state monitoring, it presented challenges related to baud rate limits and data integrity, particularly given the volume of data being transferred (30 subcarriers × 50 packets × 100 captures/second).

• AI-Powered Human Detection (Jetson Nano): Once the data was read, we deployed a custom-built, three-block Co