Breathe

  • Skills: AI, Arduino, C++, Flask, Google Maps, GPS, JavaScript, OpenAI, Python, React.js, sensors, Socket.IO, Tailwind, threading, and websockets
  • Devpost URL: Project Link

Inspiration: The Breathe project emerged from the necessity to increase environmental awareness and address the health impacts of air quality. The team recognized a lack of real-time, location-specific air quality monitoring and aimed to provide individuals with valuable data to make informed health and wellness decisions.

Functionality: Breathe integrates environmental sensing with geolocation to deliver real-time air quality monitoring. The system provides users with immediate data and AI-generated insights on how air quality influences various life aspects, such as breathability, sleep quality, and vegetation growth.

Development: The hardware integrates an Arduino Mega, a BME 680 environmental sensor, and a MAX-M10S GPS module, employing C++ for data collection. The Python backend processes the Arduino's data and transmits it via Socket.IO to a React.js frontend, which displays the information and leverages the OpenAI API for user-specific feedback.

Challenges: Key challenges included ensuring data consistency and integrity from the hardware sensors to the user interface and implementing a real-time data processing system with minimal latency, demanding thorough optimization of the Python backend.

Achievements: The team successfully merged hardware and software to offer actionable environmental insights, providing a platform that not only informs users about air quality but also educates them on its significance.

Learnings: The project was a comprehensive learning experience in areas like low-level coding with Arduino and C++, backend development with Python, and frontend technologies with React.js. The team gained insights into asynchronous programming, threading, web sockets, and the intricacies of modern web development.

Future Directions: Plans for Breathe include developing a custom PCB for a more compact and efficient design, incorporating wireless capabilities for greater accessibility, and employing data analysis and machine learning to predict air quality trends. Additionally, the team aims to develop a mobile app for real-time air quality updates and alerts.