The Quantified Self Chat

Inspiration

The growing interest in personal data tracking and the desire to leverage conversational interfaces for self-reflection and improvement. The lack of an easy way to analyze your personal data and correlate data from multiple sources. The worn-out of nurses, and the seek of developers to improve productivity.

What it does

Quantified Self Chat helps users identify optimal lifestyle adjustments based on personalized health data. By analyzing correlations between mood, sleep patterns, and exercise routines, the app suggests insights to enhance overall well-being. Users can track their health trends over time by leveraging the app's data aggregation capabilities. Quantified Self Chat offers visualizations and summaries of health metrics from different sources, allowing for informed decision-making regarding lifestyle adjustments.

How we built it

Programming Language: Python, Cloud tool: GCP, Google Cookbook, Gemini Pro Datasouce: Health Dataset (.CSV) Frontend: Streamlit, Figma

Challenges we ran into

Data Privacy and Security: Implementing measures to protect user data and ensure compliance with privacy regulations.

Accomplishments that we're proud of

Interesting insights from Gemini when running our data.

What's next for The Quantified Self Chat

Making it an app which you can also share your data with friends to keep yourself accountable Integrate as many apps as possible, not only wellness apps.