We created this app since many times when we are in situations of a medical emergency or even a survival emergency we don't know what to do. Often times panic sets in and it makes the situation out of control. Even trying to google the answer lists out hundreds of websites, most with faulty answers, and people just don't know what to believe and what to follow in an emergency. Our app RADAR helps to stop panic and create one place for users to see step by step factual information and advice.
What it does
Our app contains different scenarios and step-by-step information and guides on how to solve them such as shelter in which users can see how to make a fire and building a shelter to help survivors in the forest. Another feature is the edible scenario in which our machine learning model can use the camera to identify the berry, tell users the information about it, and whether or not it is edible. The navigation feature helps users to use nature like the stars and how to find water nearby. Finally, our medical feature helps to provide medical scenarios and step by step instructions on what to do in the situation. These features are all available offline, so a user no matter in what situation can get a factual answer at the touch of their fingertips.
How we built it
Our app was created using Flutter and Dart to create an easy user experience in a native mobile app. Using these languages we had the ability to design our idea in a clean UI for users to easily navigate the scenarios. We also used machine learning for the software used to identify edible foods in the wild.
Challenges we ran into
Some challenges we ran into were the vast amount of data we needed to put in the app which led to some problems about formatting the app to retire data from separate pages our just use 1 CSV file. Most of our issues came with designing the UI, and with lots of struggles, we finally came accords the solutions.
Accomplishments that we're proud of
Most of our team members were first-time users for using Flutter and Dart and being able to create such an intricate app for the first time was a big accomplishment. Working through each error which mainly came in the UI components we finally came around with the right solution. The machine learning model was also an accomplishment since we integrated that with our app which used machine learning to recognize certain berries. This was a major achievement since it made our app more usable to more situations for our users.
What we learned
We learned how to make a fully functioning ios/Android App and how to integrate machine learning in the app as well and working together as a team and solving errors.
What's next for RADAR
The next step for Radar is to use machine learning, simply have the user use their camera over the situation, our model should predict the best steps the user should take. Moreover, we could implement a chat feature where users can look at forums and chat with other users to learn more about survival hacks and general survival questions.