## Inspiration

It's so easy to drift from your close friends when caught up in your own world. We wanted to make a platform that allows you to stay connected easily with others and catch up with old friends to see how they're doing.

## What it does

Everyday, CatchUp will send a notification to all users to go on the app for their daily 5 minute video call with a friend. Once the app is opened, the app randomly selects someone from the user's friend list to video call and they will be able to have a short conversation and to check up on each other. CatchUp makes it easy to have a short conversation and stay connected with your friends.

## How we built it

Our group was interested in app development so chose to use Android Studio although not many of us were familiar with the platform. We had to learn the basics of coding in Kotlin, designed the UI of the app using Jetpack Compose and worked on implementing the Twilio video calling API into our app.

## Challenges we ran into

As this was the first hackathon that we've participated in, we struggled heavily with setting up and learning how to start our first project.

1. Firstly, we were unfamiliar with what platforms to use for our project. We began experimenting with Flutter and FlutterFlow to work on the UI and tried to implement it with android studio along with Twilio. We found that it was too difficult to integrate the code from Flutter with Twilio on android studio so we switched to using Jetpack Compose for the UI.
2. We struggled to set up the Twilio video calling API as it was our first time using a software like this. It took a lot of time to figure out how to download and set it up and deploy it into android studio.

## Accomplishments that we're proud of

We are all proud to say that we have completed our first ever hackathon! We are also proud of the new skills we’ve learned throughout implementing our project including using Android Studio, Jetpack Compose for creating our UI, and learning the basics of Kotlin. For most of us, it was the first time we have worked with these different softwares and we made the best of it.

## What we learned

How essential it is to be aware of what software, API's, & SDK's we can use and that are compatible with each other as soon as possible. We also learned how to use Git and Github properly and effectively. For next time, definitely setting these kinds of things up at the very beginning of a project/hackathon would definitely be the most beneficial.

## What's next for CatchUp

* Looking into integrating phone numbers/Google/Facebook for the login, and integrating firebase with this to host server information.
* Aspiring to fully complete our application with the Twilio API fully integrated and working
* Deploy the app in the Google Play store
## Inspiration

Is my child autistic? Autism is a topic that rarely, if ever, comes up in conversations and there is a severe lack of awareness about the topic. Approximately 25% of children who have autism go undiagnosed (Preidt, D, 2020). In an age where technology should innovate accessible healthcare, DetectAS is the first step before risking a large medical bill. There is a questionnaire that runs through the M-CHAT™ Modified Checklist for Autism in Toddlers (Diana L. Robins, ph. D), which is largely used by specialists. It employs facial recognition through the use of Convolutional Neural Networks which are trained to identify possible autistic traits on children from 2 to 8 years old, such as lack of eye contact or emotion. And if they do strongly suspect their kid has autism, then DetectAS provides a map of local specialists who can provide more information. We aim to ease fears and give parents the tools necessary to effectively assess whether or not their child has autism.

## What it does

DetectAS provides two primary methods of indicating whether or not a child has autism:

Facial Recognition: The parent can either open their phone camera or attach a picture from their computer. The app then scans the child's face to look for common visual indicators of autism. We used Tensorflow to implement a Convolutional Neural Network. There are 3 2D convolutional layers with 32,32 and 64 neurons with 3x3 kernels. This is followed by two fully connected dense layers. We used binary cross entropy loss and an rmsprop optimizer. We then trained our model on the 2536 train images in this dataset and were able to achieve 87.6% accuracy on the 400 test images.

Questionnaire: The parent is then directed to a questionnaire that asks about common behaviors among autistic children. There are approximately 20 questions the parent runs through, and they all require a simple yes or no answer. We included the M-Chat-R, which is a popular questionnaire that is geared towards detecting signs of autism in children and is used by field experts. We only considered simple boolean questions and did not extend further into the depths of the questionnaire. If the answer for questions 1,2,5 and 12 is yes, then the child shows signs of autism. For the other questions, the answer ‘no’ indicates signs of autism. Using this questionnaire, we calculated a probability that the child being autistic.

Resources: Once the parent has navigated through the two detection phases of DetectAS, they are then directed to the resources page. On this page, their location is pinged and the app shows a map of local autism specialists. The parent can click on any given marker to learn more information about that specialist and find contact information.

## How we built it

We used Flutter to build our app UI. In terms of our algorithms, we used Tensorflow to implement a convolutional neural network, and managed the data with binary cross entropy loss and an rmsprop optimizer. The CNN enables the facial recognition, and checks for any visual cues of autism. We also used the Google Maps API to provide the parent a list of local autism specialists.

## Challenges we ran into

Our primary challenge was the wildly different time zones each team member was in which made collaborating much more difficult than usual. We have two team members in India, one in Mexico, and one in the US. In the long run, it may have helped the project go more smoothly then one would think, because once one teammate would start to wind down for the night, another one would wake up.
We also had difficulty implementing each aspect of the project into the app once they were fully functioning in their own standing. This was mostly time consuming and an extensive process, and time is something that we do not have a lot of. Luckily, we divided the work fairly well and were able to keep up a good pace for the extent of the hackathon. Additionally, we don’t have a theoretical basis of how to combine the questionnaire and the facial detection due to our lack of expertise in the area. We considered the quantities separately to avoid any inaccuracies. If we get a positive response from either test, then we recommend consultation with a specialist. If both the responses are negative, then we predict the child shows no signs of autism. We aim to decrease the chance of false negatives from our application by combining the test results.

## Accomplishments that we're proud of

We are extremely proud of the sheer amount of work that we were able to produce in such a short amount of time. We thought our initial plans for the app were a bit ambitious, and we not only breezed through those tasks but also added more elements we thought would be useful. DetectAS is something that we think could be really useful to those who are entering the autistic community, and gives a more encompassing range of resources than the apps that are currently available. There are definitely more elements we would have like to add given the time, and the app itself could flow more, but we are proud of what we accomplished in a short amount of time!

## What we learned

Nobody on the team had experience with Flutter prior to the event, so we all enjoyed stepping out of our comfort zones. We learned a lot about how to incorporate different technologies into the Flutter app and it felt amazing once it all came together. TreeHacks provided a lot of great workshops and mentorship that we will be able to take into the future and are very grateful for!

## What's next for DetectAS

Our hope is that this app sets the tone for medical apps in the future as it provides a good range of functionalities that pertain to its focus. Given more time, we might add more functionality to the questionnaire to look at the users' answers more in depth and give more accurate results. We also would work more on improving the accuracy on our facial recognition, because there is some room for improvement to ensure DetectAS is providing accurate results. Additionally, a better database of resources and more in-depth advice as to initial steps one can take to start building a healthy environment for an autistic child would be beneficial for DetectAS.
**In times of disaster, there is an outpouring of desire to help from the public. We built a platform which connects people who want to help with people in need.**

## Inspiration

Natural disasters are an increasingly pertinent global issue which our team is quite concerned with. So when we encountered the IBM challenge relating to this topic, we took interest and further contemplated how we could create a phone application that would directly help with disaster relief.

## What it does

**Stronger Together** connects people in need of disaster relief with local community members willing to volunteer their time and/or resources. Such resources include but are not limited to shelter, water, medicine, clothing, and hygiene products. People in need may input their information and what they need, and volunteers may then use the app to find people in need of what they can provide. For example, someone whose home is affected by flooding due to Hurricane Florence in North Carolina can input their name, email, and phone number in a request to find shelter so that this need is discoverable by any volunteers able to offer shelter. Such a volunteer may then contact the person in need through call, text, or email to work out the logistics of getting to the volunteer’s home to receive shelter.

## How we built it

We used Android Studio to build the Android app. We deployed an Azure server to handle our backend(Python). We used Google Maps API on our app. We are currently working on using Twilio for communication and IBM watson API to prioritize help requests in a community.

## Challenges we ran into

Integrating the Google Maps API into our app proved to be a great challenge for us. We also realized that our original idea of including a blood donation as one of the resources would require some correspondence with an organization such as the Red Cross in order to ensure the donation would be legal. Thus, we decided to add a blood donation to our future aspirations for this project due to the time constraint of the hackathon.

## Accomplishments that we're proud of

We are happy with our design and with the simplicity of our app. We learned a great deal about writing the server side of an app and designing an Android app using Java (and Google Map’s API” during the past 24 hours. We had huge aspirations and eventually we created an app that can potentially save people’s lives.

## What we learned

We learned how to integrate Google Maps API into our app. We learn how to deploy a server with Microsoft Azure. We also learned how to use Figma to prototype designs.

## What's next for Stronger Together

We have high hopes for the future of this app. The goal is to add an AI based notification system which alerts people who live in a predicted disaster area. We aim to decrease the impact of the disaster by alerting volunteers and locals in advance. We also may include some more resources such as blood donations.