## **Inspiration**

We took inspiration from real people who have this neurological disorder. We wanted to develop an app that gives users with this disorder confidence and assurance knowing that help is on its way. The following is the epilepsy story of Brandi Parker (<http://www.cureepilepsy.org/share/my-story.asp?story=15):>

"At the age of 15 I had my first seizure. I was in high school and didn't want anyone to know that I had epilepsy. I have only had seizures on two occasions. I was so ashamed and didn't want people to think that something was wrong with me. I finally got to the point where I told people that I had epilepsy. I came to the place that I realized that I had to own my epilepsy or it would control me." -Brandy Parker

## **Epilepsy Facts**

Epilepsy is the fourth most common neurological disorder and affects people of all ages.
Epilepsy means the same thing as "seizure disorders".
Epilepsy is characterized by unpredictable seizures and can cause other health problems.
Epilepsy is a spectrum condition with a wide range of seizure types and control varying from person-to-person.

## **What is Ep-Detect?**

Ep-Detect is an Android application which is able to detect an epilepsy and informs this to a list of contacts to help prevent brain injury. It connects to a Pebble via Bluetooth which is able to detect intense shaking. Once the app recognizes that an elliptical attack is happening, it sends a text message to every contact provided with the location of the person having the attack. The app also attempts to contact emergency services. If case of a false alarm, the user will have 10 seconds to notify the app of this event.

## **How we built it**

Ep-Detect connects to a Pebble smartwatch via Bluetooth. The app stores contacts added by the user using SQLite. Once severe shaking is detected, the user is given 10 seconds to notify the app of a false alarm. Once this 10 second time period have passed, the app will get the location of the user with Google Maps and will send a text message to all contacts stored in the database using SMSManager. Also, using the Nuance Speech Kit, a warning message is triggered to alert anyone nearby of the situation. We attempted to call emergency services using a voice message using Twilio, however that feature required a paid account.

## **Challenges we ran into**

We initially ran into problems with the Pebble. At first, we weren't able to communicate with the Pebble from the app. However, we were able to find a solution using the AppMessage method. Another challenge we faced was interrupting the Nuance Speech Kit when it was playing.

## **Accomplishments that we're proud of**

We are very happy to solve the problem of detecting elliptical attacks. We were able to provide a fully functional app that can help prevent injuries and better people's lives. We were also able to make use of two APIs.

## **What we learned**

We are now more informed about this neurological disorder and we learned how technology can assist or even provide a solution to prevent injuries. Technical wise, we learned how to use the Pebble smartwatch and gain android skills. Also, we learned how to communicate and work efficiently in a team of three.

## **Next steps for Ep-Detect**

There is always room for improvement for the UI of the app. If time permitted, we would've liked to try out more APIs for the app. We would perform experiments on real users and more testing to improve the app.
## Inspiration

We were tired of the same boring jokes that Alexa tells. In an effort to spice up her creative side, we decided to implement a machine learning model that allows her to rap instead.

## What it does

Lil 'lexa uses an LSTM machine learning model to create her own rap lyrics based on the input of the user. Users first tell Alexa their rap name, along with what rapper they would like Lil 'lexa's vocabulary to be inspired by. Models have been created for Eminem, Cardi B, Nicki Minaj, Travis Scott, and Wu-Tang Clan. After the user drops a bar themselves, Lil 'lexa will spit back her own continuation along with a beat to go with it.

## How I built it

The models were trained using TensorFlow along with the Keras API. Lyrics for each rapper were scrapped from metrolyrics.com using Selenium python package, which served as the basis for the rapper's vocabulary. Fifty-word sequences were used as training data, where the model then guesses the next best word. The web application that takes in the seed text and outputs the generated lyrics is built with Flask and is deployed using Heroku. We also use Voiceflow to create the program to be loaded onto an Alexa, which uses an API call to retrieve the generated lyrics.

## Challenges I ran into

* Formatting the user input so that it would always work with the model
* Creating a consistent vocab list for each rapper
* Voiceflow inputs being merged together or stuck

## Accomplishments that I'm proud of

* My Alexa can finally gain some street cred

## What I learned

* Using Flask and Heroku to deploy an application
* Using Voiceflow to create programs that work with Amazon Alexa and Google Assistant
* Using Tensorflow to train an LSTM model

## What's next for Lil 'lexa

* Implementing more complex models that consider sentences and rhyming
* Call and response format for a rap battle
* Wider range of background beats
## Inspiration

Problem: At a recent concert a medical emergency occured in which many attendees had passed out and required immediate assistance. Due to the lack of communication between event attendees and the event security, the attendees that had passed out were not able to receive medical attention in a quick and effective manner.

Goal: Alert security when attendees have passed out at events and are unable to request assistance themselves.
Solution: A simple and effective wearable piece of technology that alerts security when an event attendee passed out and needs assistance.

## What it does

When a user is in a medical emergency and have passed out on the ground, the tilt ball sensor will activate and send a signal to the microcontroller. If the tilt ball sensor remains active for 15 seconds continuously, then the microcontroller sends a warning message to the computer.
After this, if the tilt ball sensor remains active for an additional 15 seconds (30 seconds total), the microcontroller will send an emergency message to the computer and activate the passive buzzer which will produce an alarm to notify others nearby.

## How we built it

The main components of the wearable device are:
 Arduino Uno R3 Microprocessor,
 Tilt Ball Sensor,
 Passive Buzzer.
We designed a wearable piece of technology that is:
Small - Inexpensive
Portable - Mass production
-Light weight

## Challenges we ran into

Alerting bystanders,
Debugging code

## Accomplishments that we're proud of

We are proud of prototyping a device that can support people during emergency situations.

## What we learned

We learned how to use software and hardware skills to design a prototype device.

## What's next for Faint-Alert

Add wireless capability
Easy to wear,
 Necklace form,
 Chest strap