We were inspired by the interesting topics that we attended during the first day of the Hackathon. One of the most attention grabbing topics were about the environment, that is why we also decided to work on improving the quality of life on our planet.

What it does

During the amazing 2 nights, we were able to fully design and build a mobile app, which is tracking and measuring our daily activity and impact on the world. When the user wants to go somewhere, he can run this application to know if he makes a world a better place reducing CO2. The app detects whether the user is walking, riding a bike, driving a car or taking a bus. Based on this information, it calculates how much you can reduce the CO2 in the world. There is a status bar to detect this info in real-time. The algorithm takes into account an average of the vehicle weight you are using, the total distance and the speed. To do even better and better, if you choose the right eco path, you can earn points that you can use to buy eco friendly items. Don't forget to check how many penguins you saved! :)

How we built it

We mainly used Xcode and Objective-C, Core-Location and LocationManager, to detect based on the speed, whether the user is walking, riding a bike, driving a car or taking a bus.

Challenges we ran into

As usual, we faced some challenges during the development phase, but as professional developers we were able to fix them fast and keep us focused on the idea and the project.

Accomplishments that we're proud of

We are very proud of our organisation, team work spirit, engagement in working hard during this amazing event and try to make the world a better place with our innovative mobile app. We are also very glad to have met each other, because we formed a great team, able to exchange knowledge, ideas and experience during the development phase.

What we learned

We didn't give up. If a thing doesn't work for the first time, doesn't mean that it will never work :)

What's next for eCO2

Our idea is to combine the algorithm with the Climate API to get accurate real-time estimates of the CO2 emissions. Furthermore, we can use machine learning to predict based on real data how many penguins you saved. Also we can implement the possibility to choose the type of car or bus (maybe scanning the QR code for bus) to retrieve all the information of the vehicle needed to calculate the emissions.

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