## Inspiration

We were inspired by Katie's 3-month hospital stay as a child when she had a difficult-to-diagnose condition. During that time, she remembers being bored and scared -- there was nothing fun to do and no one to talk to. We also looked into the larger problem and realized that 10-15% of kids in hospitals develop PTSD from their experience (not their injury) and 20-25% in ICUs develop PTSD.

## What it does

The AR iOS app we created presents educational, gamification aspects to make the hospital experience more bearable for elementary-aged children. These features include:

* An **augmented reality game system** with **educational medical questions** that pop up based on image recognition of given hospital objects. For example, if the child points the phone at an MRI machine, a basic quiz question about MRIs will pop-up.
* If the child chooses the correct answer in these quizzes, they see a sparkly animation indicating that they earned **gems**. These gems go towards their total gem count.
* Each time they earn enough gems, kids **level-up**. On their profile, they can see a progress bar of how many total levels they've conquered.
* Upon leveling up, children are presented with an **emotional check-in**. We do sentiment analysis on their response and **parents receive a text message** of their child's input and an analysis of the strongest emotion portrayed in the text.
* Kids can also view a **leaderboard of gem rankings** within their hospital. This social aspect helps connect kids in the hospital in a fun way as they compete to see who can earn the most gems.

## How we built it

We used **Xcode** to make the UI-heavy screens of the app. We used **Unity** with **Augmented Reality** for the gamification and learning aspect. The **iOS app (with Unity embedded)** calls a **Firebase Realtime Database** to get the user’s progress and score as well as push new data. We also use **IBM Watson** to analyze the child input for sentiment and the **Twilio API** to send updates to the parents. The backend, which communicates with the **Swift** and **C# code** is written in **Python** using the **Flask** microframework. We deployed this Flask app using **Heroku**.

## Accomplishments that we're proud of

We are proud of getting all the components to work together in our app, given our use of multiple APIs and development platforms. In particular, we are proud of getting our flask backend to work with each component.

## What's next for HealthHunt AR

In the future, we would like to add more game features like more questions, detecting real-life objects in addition to images, and adding safety measures like GPS tracking. We would also like to add an ALERT button for if the child needs assistance. Other cool extensions include a chatbot for learning medical facts, a QR code scavenger hunt, and better social interactions. To enhance the quality of our quizzes, we would interview doctors and teachers to create the best educational content.
## Inspiration

Rates of patient nonadherence to therapies average around 50%, particularly among those with chronic diseases. One of my closest friends has Crohn's disease, and I wanted to create something that would help with the challenges of managing a chronic illness. I built this app to provide an on-demand, supportive system for patients to manage their symptoms and find a sense of community.

## What it does

The app allows users to have on-demand check-ins with a chatbot. The chatbot provides fast inference, classifies actions and information related to the patient's condition, and flags when the patient’s health metrics fall below certain thresholds. The app also offers a community aspect, enabling users to connect with others who have chronic illnesses, helping to reduce the feelings of isolation.

## How we built it

We used Cerebras for the chatbot to ensure fast and efficient inference. The chatbot is integrated into the app for real-time check-ins. Roboflow was used for image processing and emotion detection, which aids in assessing patient well-being through facial recognition. We also used Next.js as the framework for building the app, with additional integrations for real-time community features.

## Challenges we ran into

One of the main challenges was ensuring the chatbot could provide real-time, accurate classifications and flagging low patient metrics in a timely manner. Managing the emotional detection accuracy using Roboflow's emotion model was also complex. Additionally, creating a supportive community environment without overwhelming the user with too much data posed a UX challenge.

## Accomplishments that we're proud of

✅deployed on defang
✅integrated roboflow
✅integrated cerebras

We’re proud of the fast inference times with the chatbot, ensuring that users get near-instant responses. We also managed to integrate an emotion detection feature that accurately tracks patient well-being. Finally, we’ve built a community aspect that feels genuine and supportive, which was crucial to the app's success.

## What we learned

We learned a lot about balancing fast inference with accuracy, especially when dealing with healthcare data and emotionally sensitive situations. The importance of providing users with a supportive, not overwhelming, environment was also a major takeaway.

## What's next for Muni

Next, we aim to improve the accuracy of the metrics classification, expand the community features to include more resources, and integrate personalized treatment plans with healthcare providers. We also want to enhance the emotion detection model for more nuanced assessments of patients' well-being.
## Inspiration

Often times health information is presented in a very clinical manner that is unfriendly for kids, there's too much medical jargon and children have difficulty engaging with the information. We wanted to turn learning about things like diabetes, first aid, and other medical ailments into a fun and interactive experience by utilizing superheroes! Our goal is to help spread awareness and educate students about various health topics in a fun and exciting manor.

## What it does

Marvel Medical Dictionary (MMD) is an mobile augmented reality experience that allows users to learn about different health topics from their favorite Marvel superheroes. After searching a topic like diabetes or spider bites, MMD utilizes natural language processing using data from the Marvel API to select a super hero that is closely related to the search query. For example, if our bite-sized hero/heroine searched for spider bites Spider-Man would be there to provide easily understandable information. Users are able to "watch" Spider-man and other Avengers on their mobile device and learn about different types of health issues.

## How we built it

We built MMD on the Unity Game Engine, C#, and imported 3D models found online. We also utilized flask's python framework to retrieve health information, Marvel API data, as well as run our natural language processing code.

## Challenges we ran into

A little bit of everything. We have limited experience with Unity and have never developed in AR before. This is also our first time working with web development and had a lot of \_ fun \_ trying to find bugs in our python code as well as learning the beauty of JavaScript and its relation with HTML.

## What's next for Marvel Medical Dictionary

Moving forward we would love to integrate more Marvel character models and add more dynamic movements and animations to the AR experience. We also want to more closely integrate engagement principles to increase information retention in regards to health knowledge. Although, MMD was just an idea created on a whim, in the future it could bring awareness and health education to young children all across the world.