## Inspiration

The inspiration behind "Nurture AI" stemmed from understanding the unique challenges faced by parents, especially those of special needs children. Recognizing that constant supervision can be both exhausting and impractical, we sought to create a tool that would assist in monitoring their child’s emotional and mental state, especially during times when direct supervision isn't possible. This tool is not only meant to offer peace of mind but also aims to foster independence in children, giving them the confidence to engage in activities like homework or play, knowing they're still under a watchful, caring eye.

## What it does

Nurture AI uses AI-driven facial recognition and emotion detection technology through a simple webcam to analyze a child's emotional state in real-time. The app recognizes a range of emotions – from distress and frustration to joy and contentment. When it detects negative emotional shifts or signs of distress, it promptly notifies the parent or caregiver via text. This feature ensures that the caregiver can intervene quickly when necessary, yet also allows them to manage other tasks without constant physical supervision.

## How we built it

We used both Hume, Reflex, Twillo, and Redis to make this project possible.

## Challenges we ran into

Making the user interface friendly for children who may struggle with autism or epilepsy.

Balancing Sensitivity and Specificity: Adjusting the system to correctly interpret emotions without overloading the parent with notifications.

User Trust and Privacy: Ensuring the application maintains high standards of user privacy and security, crucial for building trust.

## Accomplishments that we're proud of

It works! It very accurately can send texts when someone is stressed or any other emotion.

## What's Next for Nurture

By continuously evolving and adapting, we aim to ensure that Nurture AI not only keeps pace with technological advancements and user expectations but also extends its reach and positive impact on more families and communities. We hope to add more solutions to help children calm down after detection, and make it adult-oriented too so we can check our own emotions.
## Inspiration

Everyone can relate to the scene of staring at messages on your phone and wondering, "Was what I said toxic?", or "Did I seem offensive?". While we originally intended to create an app to help neurodivergent people better understand both others and themselves, we quickly realized that emotional intelligence support is a universally applicable concept.

After some research, we learned that neurodivergent individuals find it most helpful to have plain positive/negative annotations on sentences in a conversation. We also think this format leaves the most room for all users to reflect and interpret based on the context and their experiences. This way, we hope that our app provides both guidance and gentle mentorship for developing the users' social skills. Playing around with Co:here's sentiment classification demo, we immediately saw that it was the perfect tool for implementing our vision.

## What it does

IntelliVerse offers insight into the emotions of whomever you're texting. Users can enter their conversations either manually or by taking a screenshot. Our app automatically extracts the text from the image, allowing fast and easy access. Then, IntelliVerse presents the type of connotation that the messages convey. Currently, it shows either a positive, negative or neutral connotation to the messages. The interface is organized similarly to a texting app, ensuring that the user effortlessly understands the sentiment.

## How we built it

We used a microservice architecture to implement this idea
The technology stack includes React Native, while users' information is stored with MongoDB and queried using GraphQL. Apollo-server and Apollo-client are used to connect both the frontend and the backend.
The sentiment estimates are powered by custom Co:here's finetunes, trained using a public chatbot dataset found on Kaggle.
Text extraction from images is done using npm's text-from-image package.

## Challenges we ran into

We were unfamiliar with many of the APIs and dependencies that we used, and it took a long to time to understand how to get the different components to come together.
When working with images in the backend, we had to do a lot of parsing to convert between image files and strings.
When training the sentiment model, finding a good dataset to represent everyday conversations was difficult. We tried numerous options and eventually settled with a chatbot dataset.

## Accomplishments that we're proud of

We are very proud that we managed to build all the features that we wanted within the 36-hour time frame, given that many of the technologies that we used were completely new to us.

## What we learned

We learned a lot about working with React Native and how to connect it to a MongoDB backend. When assembling everyone's components together, we solved many problems regarding dependency conflicts and converting between data types/structures.

## What's next for IntelliVerse

In the short term, we would like to expand our app's accessibility by adding more interactable interfaces, such as audio inputs. We also believe that the technology of IntelliVerse has far-reaching possibilities in mental health by helping introspect upon their thoughts or supporting clinical diagnoses.
## 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.