Our inspiration first came from various stories about how people would listen to certain apps that gives a single suggestion to purchase an item that they already have. It usually suggest stuff that's in their fridge but a little pricier, or suggest you an item that has an x amount of nutritional value as a different item that you already own that has the same x amount of nutritional value. Our group felt strongly about this situation and wants to give the masses more options to choose from, based on what's in their fridge.

What it does:

Our software works with leading IoT technology that offers you the smart fridge that will keep track of the calories you burnt daily and suggest you food that will maximize the benefits for your body. The system uses facial recognition to build your own personal profile and will identify you from the other users in your home. There is also a camera inside the fridge that looks for what's inside the fridge. It can tell you the amount of calories of each item in your fridge and calculate the best choices to intake. This fridge is also able to give you multiple suggestions for what food to purchase whenever you are at the grocery store, based on what's already in your fridge, along with special deals offered just for you as an incentive for you to purchase the suggested item.

There is also an android app that you can use in with the smart fridge. It tracks how many steps you walked and then calculates the amount of calories you burnt. There is also a system that works with the IoT that has face recognition and displays your profile. The camera inside the fridge looks for what's inside the fridge and the app then pushes a notification on multiple suggestions what you should eat to maintain your diet.

How we built it

The software was created using Python as the back-end and android-studio/java as front-end.

Challenges we ran into

We spent most of our time attempting to use a Raspberry Pi, Dragon Board, and as well as getting Gradle to work. Unfortunately, we were not able to figure out how to use the Raspberry Pi or Dragon Board but we were able to get Gradle working.

Accomplishments that we're proud of

We are proud of getting Gradle to actually work and for the accurateness of the software to identify what the food is. .

What we learned

We learned how machine learning algorithm worked.

What's next for APX

We could see this product extending its influence between other IoT devices, be the primary source for every nutritious conscious person, and eventually dominate the world of fitness.

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