## Inspiration

Moving away from home and being responsible for my spending was not easy, especially in a new country. After a while, it was evident that I was not alone in struggling to manage my money: fellow students, co-workers; the list can go on.

## What it does

The goal was to get the user's income information as well as their most common monthly spending, and that data would be used to help identify potential risks and offer advice. At the moment, the only thing that you can do is input your personal income and you can see where your income goes after tax

## How I built it

I decided to tackle this challenge with a website using HTML, CSS, and JavaScript.

## Challenges I ran into

I may have been too ambitious and overestimated what I could do alone in the timeframe that I had : ) I could not do everything I wanted, but I was happy to do enough for some sort of demo

## Accomplishments that I'm proud of

My knowledge of HTML, CSS, and JavaScript was relatively novice, so I'm happy with what I did in about 2 days (I have about a million tabs opened!). I'm especially proud of how I implemented JavaScript in the project to get the algorithms

## What I learned

HTML, CSS, and JavaScript aside, I learned a lot about my limits and how far I can go with limited time and limited knowledge

## What's next for Budget Ur Bred

I definitely want to continue working on this project as it does have some relevance in my life. I'm going to continue developing my skills, with an emphasis on CSS
# Musr

By using the Muse headband, we are able to automate the autoswipe feature for Tinder solely based on brain wave data.

The Muse headband takes in brain EEG data which we can analyze further. The headband has four nodes located near the left ear, left forehead, right forehead, and right ear. These nodes collected raw EEG data every second. The raw data can be seen in image #1. Wearing the Muse headband, the user's EEG is being tracked, however in order for it to be useful to us, it must be filtered (see image #2). The algorithm runs the raw data through a FFT (Fast Fourier Transformation) to eliminate volatile data.

From there, the data is broken down into the different wavelengths - delta, theta, alpha, beta, and gamma, respectively lengthwise. This can be seen in image #3. By consulting with a neurotech engineer at midnight, we've determined that "attraction" at the most primitive level can be measured by extracting the alpha value from the PSD (Power spectral density). The next step would be to test this on a subject (a friend).

Next we started to explore relationships between these brainwave values and the subject's preferences. To do so, the subject viewed samples that they deemed attractive for 90 seconds. Their brain EEG data was recorded. Next, the subject looked at samples that they deemed unattractive for 90 seconds.With this experiment we were able to gather valuable data. We compared the ratios between the types of brainwaves for each node, the averages between nodes, and even the variances in our data. Later we instantiated a calibration portion to determine a baseline to compare our brainwaves to. After some fiddling with the numbers, alpha brainwaves that were below this calibration average are understood as the subject is attracted to a sample and alpha brainwave readings above the average is understood as the subject does not deem the sample attractive.

Later we decided to disregard the readings for all brain waves except for alpha waves because they seemed to be the most prominent indicator of attraction.

# Problems we had

* Navigating an undocumented Tinder API
* Retrieving the Tinder Auth API by reading the auth\_token logs from our sessions
* Creating an algorithm to determine how attracted you are to the person

# things we've found

* When we're more calm and focused, the results are fairly accurate

# Fun moment's we've had

* Validating our "swipe right" feature by matching with each other
* Receiving matches from confused men
* "Swiping left" on people we find attractive
* Finding out each other's tastes
## Inspiration

A couple weeks ago, a friend was hospitalized for taking Advil–she accidentally took 27 pills, which is nearly 5 times the maximum daily amount. Apparently, when asked why, she responded that thats just what she always had done and how her parents have told her to take Advil. The maximum Advil you are supposed to take is 6 per day, before it becomes a hazard to your stomach.

#### PillAR is your personal augmented reality pill/medicine tracker.

It can be difficult to remember when to take your medications, especially when there are countless different restrictions for each different medicine. For people that depend on their medication to live normally, remembering and knowing when it is okay to take their medication is a difficult challenge. Many drugs have very specific restrictions (eg. no more than one pill every 8 hours, 3 max per day, take with food or water), which can be hard to keep track of. PillAR helps you keep track of when you take your medicine and how much you take to keep you safe by not over or under dosing.

We also saw a need for a medicine tracker due to the aging population and the number of people who have many different medications that they need to take. According to health studies in the U.S., 23.1% of people take three or more medications in a 30 day period and 11.9% take 5 or more. That is over 75 million U.S. citizens that could use PillAR to keep track of their numerous medicines.

## How we built it

We created an iOS app in Swift using ARKit. We collect data on the pill bottles from the iphone camera and passed it to the Google Vision API. From there we receive the name of drug, which our app then forwards to a Python web scraping backend that we built. This web scraper collects usage and administration information for the medications we examine, since this information is not available in any accessible api or queryable database. We then use this information in the app to keep track of pill usage and power the core functionality of the app.

## Accomplishments that we're proud of

This is our first time creating an app using Apple's ARKit. We also did a lot of research to find a suitable website to scrape medication dosage information from and then had to process that information to make it easier to understand.

## What's next for PillAR

In the future, we hope to be able to get more accurate medication information for each specific bottle (such as pill size). We would like to improve the bottle recognition capabilities, by maybe writing our own classifiers or training a data set. We would also like to add features like notifications to remind you of good times to take pills to keep you even healthier.