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PulseTrack: Smart Wearable Health & Sustainability Tracker

Inspiration

We wanted to build a wearable that does more than just track health—it should empower users with real-time insights while promoting sustainability. The idea came from recognizing the importance of personal health monitoring and how daily activity, like walking, contributes to reducing carbon emissions. By integrating AI-driven analysis, we aimed to provide users with actionable insights for both their well-being and environmental impact.

What It Does

PulseTrack is a real-time wearable health and sustainability tracker that monitors:

Heart rate & blood oxygen (MAX30100/02)
Body temperature (MLX90614)
Step count & motion analysis (MPU6050)
Energy expenditure & carbon reduction based on steps

Data is transmitted from an ESP32 microcontroller to a Flask WebSocket server, where it is processed and displayed in a Next.js web application for real-time visualization. Additionally, Google Gemini AI provides personalized health insights and an "Ask AI" feature for user queries.

How We Built It

Hardware Setup: Integrated sensors with the ESP32 for continuous data collection.
Backend Development: Built a Flask WebSocket server to handle real-time data transmission and storage in SQLite.
Frontend Development: Used Next.js for a dynamic and interactive web dashboard.
AI Integration: Incorporated Google Gemini AI to analyze health data and provide personalized recommendations.

Challenges We Ran Into

The toughest challenge was establishing real-time communication between the ESP32 and the frontend. We spent 8+ hours debugging the data pipeline, ensuring smooth transmission from the microcontroller → Flask server → WebSocket → frontend UI. Additionally, making the hardware compact and wearable required careful planning in sensor placement and power management.

Accomplishments That We're Proud Of

Successfully built a real-time health tracking system from scratch.
Integrated AI-powered health insights using Google Gemini.
Developed a seamless WebSocket-based data flow between hardware and software.
Engineered a wearable device that balances functionality, efficiency, and usability.

What We Learned

Hardware-to-software integration – Connecting ESP32 sensor data to a real-time web app.
AI-powered analytics – Using GenAI to generate meaningful health insights.
Making hardware wearable – Designing a compact, efficient, and user-friendly device.