View the SlideDeck for this project at: [slides](https://docs.google.com/presentation/d/1G1M9v0Vk2-tAhulnirHIsoivKq3WK7E2tx3RZW12Zas/edit?usp=sharing)

## Inspiration / Why

It is no surprise that mental health has been a prevailing issue in modern society. 16.2 million adults in the US and 300 million people in the world have depression according to the World Health Organization. Nearly 50 percent of all people diagnosed with depression are also diagnosed with anxiety. Furthermore, anxiety and depression rates are a rising issue among the teenage and adolescent population. About 20 percent of all teens experience depression before they reach adulthood, and only 30 percent of depressed teens are being treated for it.

To help battle for mental well-being within this space, we created DearAI. Since many teenagers do not actively seek out support for potential mental health issues (either due to financial or personal reasons), we want to find a way to inform teens about their emotions using machine learning and NLP and recommend to them activities designed to improve their well-being.

## Our Product:

To help us achieve this goal, we wanted to create an app that integrated journaling, a great way for users to input and track their emotions over time. Journaling has been shown to reduce stress, improve immune function, boost mood, and strengthen emotional functions. Journaling apps already exist, however, our app performs sentiment analysis on the user entries to help users be aware of and keep track of their emotions over time.

Furthermore, every time a user inputs an entry, we want to recommend the user something that will lighten up their day if they are having a bad day, or something that will keep their day strong if they are having a good day. As a result, if the natural language processing results return a negative sentiment like fear or sadness, we will recommend a variety of prescriptions from meditation, which has shown to decrease anxiety and depression, to cat videos on Youtube. We currently also recommend dining options and can expand these recommendations to other activities such as outdoors activities (i.e. hiking, climbing) or movies.

**We want to improve the mental well-being and lifestyle of our users through machine learning and journaling.This is why we created DearAI.**

## Implementation / How

Research has found that ML/AI can detect the emotions of a user better than the user themself can. As a result, we leveraged the power of IBM Watson’s NLP algorithms to extract the sentiments within a user’s textual journal entries. With the user’s emotions now quantified, DearAI then makes recommendations to either improve or strengthen the user’s current state of mind. The program makes a series of requests to various API endpoints, and we explored many APIs including Yelp, Spotify, OMDb, and Youtube. Their databases have been integrated and this has allowed us to curate the content of the recommendation based on the user’s specific emotion, because not all forms of entertainment are relevant to all emotions.

For example, the detection of sadness could result in recommendations ranging from guided meditation to comedy. Each journal entry is also saved so that users can monitor the development of their emotions over time.

## Future

There are a considerable amount of features that we did not have the opportunity to implement that we believe would have improved the app experience. In the future, we would like to include video and audio recording so that the user can feel more natural speaking their thoughts and also so that we can use computer vision analysis on the video to help us more accurately determine users’ emotions. Also, we would like to integrate a recommendation system via reinforcement learning by having the user input whether our recommendations improved their mood or not, so that we can more accurately prescribe recommendations as well. Lastly, we can also expand the APIs we use to allow for more recommendations.
## Inspiration

We got together a team passionate about social impact, and all the ideas we had kept going back to loneliness and isolation. We have all been in high pressure environments where mental health was not prioritized and we wanted to find a supportive and unobtrusive solution. After sharing some personal stories and observing our skillsets, the idea for Remy was born. **How can we create an AR buddy to be there for you?**

## What it does

**Remy** is an app that contains an AR buddy who serves as a mental health companion. Through information accessed from "Apple Health" and "Google Calendar," Remy is able to help you stay on top of your schedule. He gives you suggestions on when to eat, when to sleep, and personally recommends articles on mental health hygiene. All this data is aggregated into a report that can then be sent to medical professionals. Personally, our favorite feature is his suggestions on when to go on walks and your ability to meet other Remy owners.

## How we built it

We built an iOS application in Swift with ARKit and SceneKit with Apple Health data integration. Our 3D models were created from Mixima.

## Challenges we ran into

We did not want Remy to promote codependency in its users, so we specifically set time aside to think about how we could specifically create a feature that focused on socialization.

We've never worked with AR before, so this was an entirely new set of skills to learn. His biggest challenge was learning how to position AR models in a given scene.

## Accomplishments that we're proud of

We have a functioning app of an AR buddy that we have grown heavily attached to. We feel that we have created a virtual avatar that many people really can fall for.

## What we learned

Aside from this being many of the team's first times work on AR, the main learning point was about all the data that we gathered on the suicide epidemic for adolescents. Suicide rates have increased by 56% in the last 10 years, and this will only continue to get worse. We need change.

## What's next for Remy

While our team has set out for Remy to be used in a college setting, we envision many other relevant use cases where Remy will be able to better support one's mental health wellness.

Remy can be used as a tool by therapists to get better insights on sleep patterns and outdoor activity done by their clients, and this data can be used to further improve the client's recovery process. Clients who use Remy can send their activity logs to their therapists before sessions with a simple click of a button.

To top it off, we envisage the Remy application being a resource hub for users to improve their overall wellness. Through providing valuable sleep hygiene tips and even lifestyle advice, Remy will be the one-stop, holistic companion for users experiencing mental health difficulties to turn to as they take their steps towards recovery.
## Inspiration

Mental health is a serious issue but also one hard to diagnose and address. In these times of our virtual environments and uncertainties, people tend to face more difficulty and strain on their mental health. We aim to help individuals become more aware and hopefully, identify signs of mental health issues.

We realized that not too many people look after their mental health until it's far too late. As such, we decided to implement a solution that motivated people to write down their thoughts so that they could track their mood over time. Other mental health tracking apps exist on the market right now, but none utilize NLP to take advantage of the actual writing part of journalling. That is how our idea of Mood was born!

## What it does

Mood is a journaling app. It allows the user to make quick daily entries about how they feel and what significant events occurred. Using this text entry, the app performs sentiment analysis to rate the current mood of the user and tracks it over time. This enables the app to identify trends in the user's mental health daily, weekly, monthly, and overall. It also calculates the most frequently occurring emotional keywords and general keywords throughout a period, on top of the top positive and negative keywords that make the user smile and cry the most.

## How we built it

We built the interface of Mood using Flutter. Our team decided that for a journaling application, it would be more accessible if it was available on mobile. The sentiment analysis is done using Python and its NLP libraries like textblob, spacy, nltk, and transformers. Since the backend is written in Python, we decided to use Flask to manage our back-end server. These are connected with a REST API.

## Challenges we ran into

We all had to learn a new technology for this project and was definitely one of the biggest challenges that we faced. Another big challenge that we ran into is creating APIs to connect our front-end to the back-end.

## Accomplishments that we're proud of

Our team has a lot to be proud of. We all learned something new and got something to take away from this Hackathon. Even though we stayed up really late, we are happy that we have something to show to the judges.

## What we learned

One of the biggest lessons that we learned from this experience is how important the initial development plan is. Running into problems later on caused our team to spend more time to find a workaround to problems that in hindsight could have been better planned for.

## What's next for Mood

Mood has lots of room for future improvement. Features such as user authentication and cloud storage would be great additions to improve the security and protect the privacy of our users. Also, notification systems on long-term and short-term abnormal trends (bipolar, depression,...) of mood would be meaningful for users to understand their emotional behaviour. Other improvements would include a more accurate and advanced model of sentimental analysis and improving accessibility of the interface (i.e. voice or image input).
An online system that learns the users’ mood patterns and responds better to changes.