## Inspiration

Currently, the process to get medical information from your healthcare provider is intimidating and can be quite a pain for many users. Hoping to solve this problem, we decided to create an app that would make this process easy and natural while also providing additional comprehensive information on personal health.

## What it does

hospital.ity "makes the hospital hospitable" – it enables people from all walks of life to retrieve information from their healthcare providers and inform themselves about their personal health with ease. Users can access everything from blood oxygen levels to prescriptions to medical encounter history. Furthermore, with its chatbot interface, hospital.ity is extremely user-friendly, allowing people to find the information they want with a simple question.

## How we built it

We built the app using Objective-C, queried the Wolfram Alpha and Human API for personal wellness/medical data via REST API calls, and trained the natural language processing (NLP) model using wit.ai. Using image recognition algorithms, the app also provides nutritional information for food in a photograph.

## Challenges we ran into

We actually changed our focus halfway through the hackathon from food/nutrition to overall health after discovering the Human API. However, as we were unfamiliar with the API, which retrieves medical information from users, we initially struggled with authentication in accessing user data. We also had some difficulty with handling the dozens of possible queries in wit.ai – each potential question required a large number of NLP training cases (each sometimes requiring a large amount of time to learn) for it to work properly in response to conversational speech. And of course, it was quite the challenge to parse API data and produce the proper response on screen.

## Accomplishments that we're proud of

Making the process of finding health information friendly and extremely accessible for the average person. On the technical side, developing a robust intent identification with NLP and developing algorithms to parse the data for the chatbot.

## What we learned

We learned how to train an NLP model via semantic analysis and how to connect it to multiple APIs to create a functioning chatbot.

## What's next for hospital.ity

We didn't end up developing for all the queries trained in wit.ai due to time constraints, so there's definitely more to add there. In addition to implementing more possible queries and strengthening the NLP model, hospital.ity could extend to allowing the user to perform actions such as setting up appointments. This would ease users' interactions with their healthcare providers even more, furthering our goal of making personal health important and understandable for all.
## Inspiration

What is our first thought when we hear "health-care"? Is it an illness? Cancer? Disease? That is where we lose our focus from an exponential increasing crisis, especially in this post-COVID era. It is MENTAL HEALTH!

Studying at university, I have seen my friends suffer from depression and anxiety looking for someone to hear them out for once.

Statistically, an estimated 792 million individuals worldwide suffer from mental health diseases and concerns.
That's roughly one out of every ten persons on the planet.

In India, where I am from, the problem is even worse. Close to 14 per cent of India's population required active mental health interventions. Every year, about 2,00,000 Indians take their lives. The statistics are even higher if one starts to include the number of attempts to suicide.

The thought of being able to save even a fraction of this number is powerful enough to get me working this hard for it.

## What it does

Noor, TALKs because that's all it takes. She provides a comfortable environment to the user where they can share their thoughts very privately, and let that feelings out once and for all.

## How we built it

I built this app in certain steps:

1. Converting all the convolutional intents into a Machine Learning Model - Pytorch.
2. Building a framework where users can provide input and the model can output the best possible response that makes the most sense. Here, the threshold is set to 90% accuracy.
3. Building an elegant GUI.

## Challenges we ran into

Building Chatbots from scratch is extremely difficult. However, 36 hours were divided into sections where I could manage building a decent hack out of everything I got. Enhancing the bot's intelligence was challenging too. In the initial stages, I was experimenting with fewer intents, but with the addition of more intents, keeping track of the intents became difficult.

## Accomplishments that we're proud of

First, I built my own Chatbot for the first time!!! YAYYYYY! This is a really special project because of the fact that it is dealing with such a major issue in the world right now. Also, this was my first time making an entire hackathon project using Python and its frameworks only. Extremely new experience. I am proud of myself for pushing through the frustrating times when I felt like giving up.

## What we learned

Everything I made during this hackathon was something I had never done before. Legit, EVERYTHING! Let it be using NLP or Pytorch. Or even Tkinter for Graphic User Interface (GUI)! Honestly, may not be my best work ever but, definitely something that taught me the most!

## What's next for Noor

Switch from Tkinter to deploying my script into an application or web app. The only reason I went with Tkinter was to try learning something new. I'll be using flutter for app development and TFjs for a web-based application.

Discord: keivalya#8856
## Inspiration

* COVID-19 is impacting all musicians, from students to educators to professionals everywhere
* Performing physically together is not viable due to health risks
* School orchestras still need a way to perform to keep students interested and motivated
* A lot of effort is required to put together separate recordings virtually. Some ensembles don't have the time or resources to do it

## What it does

Ludwig is a direct response to the rise of remote learning. Our online platform optimizes virtual interaction between music educators and students by streamlining the process of creating music virtually. Educators can create new assignments and send them out with a description and sheet music to students. Students can then access the assignment, download the sheet music, and upload their recordings of the piece separately. Given the tempo and sampling specificity, Ludwig detects the musical start point of each recording, then syncs and merges them into one combined WAV file.

## Challenges we ran into

Our synchronization software is not perfect. On the software side, we have to balance the tradeoff between sampling specificity and delivery speed, so we sacrifice pinpoint synchronization to make sure our users don't get bored while using the platform. On the human side, without the presence of the rest of the ensemble, it is easy for students to play at an inconsistent tempo, play out of tune, or make other mistakes. These sorts of mistakes are hard for any software to adapt to.

## What's next for Ludwig

We aim to improve Ludwig's syncing algorithm by adjusting tuning and paying more attention to tempo. We will also refine and expand Ludwig's platform to allow teachers to have different classes with different sets of students.