## Inspiration

We were interested in seeing how specific items had an impact on our earth and what we could do differently to reduce negative impacts. We were interested after seeing that H&M had faced a lawsuit about false sustainability claims. However, if you have something that truly shows you how sustainable or unsustainable something is, you become more aware of your choices and where you buy from.

## What it does

This google extension is intended to inspect the current product a user is viewing and using the material make up of the product, give a material sustainability index. The base material sustainability index (MSI) is a number that represents how sustainable a base materials is, and it is determined based on several factors, such as how long the material last, or resourced used to make it. The MSI is calculated based on the proportion of the materials used in a product. Then, we calculated the overall sustainability index by adding together the MSI of material used.

## How we built it

We used Javascript, HTML, and CSS to build it. We attempted web scraping by looking at different resources that ran through it using node.js, axios, and cheerio.

## Challenges we ran into

We ran into challenges when attempting to web scrape. We never found out how to properly do it. :(

## Accomplishments that we're proud of

We are proud of creating a google extension, something we had never done before. We both do not have a lot of front-end experience, so it was nice to have created at least a graphic for a google extension.

## What we learned

We learned that web scraping is very useful but can be tricky to work with. Neither of us had heard of web scraping before this, so it was interesting to learn about and attempt.

## What's next for Earth's Closet

We are hoping to get it to work eventually. Hopefully add some cooler aspects such as adding in more sustainable alternative solutions, like listing an item that is currently being sold second-hand, either identical to what the user is looking for or similar.
## Inspiration

Our inspiration comes from our own experiences as programmers, where we realized that it was sometimes difficult (and a productivity drain), to move our right hands between our keyboard and our mouse. We wondered whether there was any possibility of redesigning a new version of the mouse to work without the need to move our hand from the keyboard.

## What it does

nullHands utilizes a variety of measurements to provide users with an accurate mouse movement system. First, we utilize a gyroscope to grab the user's head movements, mapping that to the mouse movements on the screen. Then, we monitored the user's eyes, mapping the left and right eyes to the left and right respective mouse clicks.

## How we built it

We built this using iOS Swift 3, to monitor the gyroscope. We then used Python with sockets to run the backend, as well as to run the OpenCV and Mouse movement libraries

## Challenges we ran into

We did not have the hardware we needed, so we had to improvise with an iPhone, as well as a headband.

## Accomplishments that we're proud of

We are proud of managing to create a working prototype in under 24 hours.

## What we learned

We learned about sockets and direct communication between iOS and computer.

## What's next for nullHands

We are really passionate about nullHands, and think that this is a project that can definitely help a lot of people. Therefore, we plan on continuing to work on nullHands, improving and adding functionality so that we can one day release this as a product so that everyone can experience nullHands.
## Inspiration

Like most university students, we understand and experience the turbulence that comes with relocating every 4 months due to coop sequences while keeping personal spendings to a minimum. It is essential for students to be able to obtain an affordable and effortless way to release their stresses and have hobbies during these pressing times of student life.

## What it does

AirDrum uses computer vision to mirror the standard drum set without the need for heavy equipment, high costs and is accessible in any environment.

## How we built it

We used python (NumPy, OpenCV, MatPlotLib, PyGame, WinSound) to build the entire project.

## Challenges we ran into

The documentation for OpenCV is less robust than what we wanted, which lead to a lot of deep dives on Stack Overflow.

## Accomplishments that we're proud of

We're really happy that we managed to actually get something done.

## What we learned

It was our first time ever trying to do anything with OpenCV, so we learned a lot about the library, and how it works in conjunction with NumPy.

## What's next for AirDrums

The next step for AirDrums is to add more functionality, allowing the user to have more freedom with choosing which drums parts they would like and to be able to save beats created by the user. We also envision a guitar hero type mode where users could try to play the drum part of a song or two. We could also expand to different instruments.