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The Backend running with the activation of streamlit but in the background
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The Dashboard when AURION is sleeping(OFF MODE)
Inspiration We were inspired by the idea that computers can monitor system performance like CPU, memory, and network usage — but they have no awareness of the human operating them. We wanted to explore whether cognitive states like focus, stress, and intensity could be logged and visualized as telemetry, just like system metrics.
What it does AURION is a cognitive telemetry dashboard that logs and visualizes mental‑state signals over time. It records cognitive state entries, tracks intensity trends, shows state distributions, and generates simple daily summaries to help users understand their behavioral patterns.
How we built it We built AURION using Python and Streamlit as a rapid prototyping stack. Cognitive telemetry data is logged into structured JSON files, analyzed using Python utilities, and visualized through an interactive Streamlit dashboard. We also experimented with OpenCV‑based vision input as an early step toward real‑time sensing.
Challenges we ran into One of the biggest challenges was designing a reliable telemetry logging pipeline and structuring cognitive data in a meaningful way. We also faced difficulties integrating vision modules, dependency management (like OpenCV), and real‑time visualization workflows.
Accomplishments that we're proud of We successfully built a working cognitive telemetry prototype that logs daily cognitive states, generates summaries, and visualizes behavioral patterns. Turning an abstract idea like “mental state monitoring” into a functional dashboard system was a major milestone for us.
What we learned We learned how to design data logging pipelines, dashboards, and cognitive state models, and how important iteration is when moving from an idea to a working prototype. We also learned how to structure projects so they can evolve from experimental dashboards into scalable systems.
What's next for AURION Next, we plan to move beyond the Streamlit prototype and build a real‑time architecture with a dedicated frontend and backend, live sensor input, and streaming telemetry. Our long‑term goal is to evolve AURION into a cognitive‑aware interface system that adapts to the user in real time.
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