## Inspiration

So many people around the world, including those dear to us, suffer from mental health issues such as depression. Here in Berkeley, for example, the resources put aside to combat these problems are constrained. Journaling is one method commonly employed to fight mental issues; it evokes mindfulness and provides greater sense of confidence and self-identity.

## What it does

SmartJournal is a place for people to write entries into an online journal. These entries are then routed to and monitored by a therapist, who can see the journals of multiple people under their care. The entries are analyzed via Natural Language Processing and data analytics to give the therapist better information with which they can help their patient, such as an evolving sentiment and scans for problematic language. The therapist in turn monitors these journals with the help of these statistics and can give feedback to their patients.

## How we built it

We built the web application using the Flask web framework, with Firebase acting as our backend. Additionally, we utilized Microsoft Azure for sentiment analysis and Key Phrase Extraction. We linked everything together using HTML, CSS, and Native Javascript.

## Challenges we ran into

We struggled with vectorizing lots of Tweets to figure out key phrases linked with depression, and it was very hard to test as every time we did so we would have to wait another 40 minutes. However, it ended up working out finally in the end!

## Accomplishments that we're proud of

We managed to navigate through Microsoft Azure and implement Firebase correctly. It was really cool building a live application over the course of this hackathon and we are happy that we were able to tie everything together at the end, even if at times it seemed very difficult

## What we learned

We learned a lot about Natural Language Processing, both naively doing analysis and utilizing other resources. Additionally, we gained a lot of web development experience from trial and error.

## What's next for SmartJournal

We aim to provide better analysis on the actual journal entires to further aid the therapist in their treatments, and moreover to potentially actually launch the web application as we feel that it could be really useful for a lot of people in our community.
## Overview

We created a Smart Glasses hack called SmartEQ! This unique hack leverages the power of machine learning and facial recognition to determine the emotion of the person you are talking to and conducts sentiment analysis on your conversation, all in real time! Since we couldn’t get a pair of Smart Glasses (*hint hint* MLH Hardware Lab?), we created our MVP using a microphone and webcam, acting just like the camera and mic would be on a set of Smart Glasses.

## Inspiration

Millions of children around the world have Autism Spectrum Disorder, and for these kids it can be difficult to understand the emotions people around them. This can make it very hard to make friends and get along with their peers. As a result, this negatively impacts their well-being and leads to issues including depression. We wanted to help these children understand others’ emotions and enable them to create long-lasting friendships. We learned about research studies that wanted to use technology to help kids on the autism spectrum understand the emotions of others and thought, hey, let’s see if we could use our weekend to build something that can help out!

## What it does

SmartEQ determines the mood of the person in frame, based off of their facial expressions and sentiment analysis on their speech. SmartEQ then determines the most probable emotion from the image analysis and sentiment analysis of the conversation, and provides a percentage of confidence in its answer. SmartEQ helps a child on the autism spectrum better understand the emotions of the person they are conversing with.

## How we built it

The lovely front end you are seeing on screen is build with React.js and the back end is a Python flask server. For the machine learning predictions we used a whole bunch of Microsoft Azure Cognitive Services API’s, including speech to text from the microphone, conducting sentiment analysis based off of this text, and the Face API to predict the emotion of the person in frame.

## Challenges we ran into

Newton and Max came to QHacks as a confident duo with the initial challenge of snagging some more teammates to hack with! For Lee and Zarif, this was their first hackathon and they both came solo. Together, we ended up forming a pretty awesome team :D. But that’s not to say everything went as perfectly as our new found friendships did. Newton and Lee built the front end while Max and Zarif built out the back end, and as you may have guessed, when we went to connect our code together, just about everything went wrong. We kept hitting the maximum Azure requests that our free accounts permitted, encountered very weird socket-io bugs that made our entire hack break and making sure Max didn’t drink less than 5 Red Bulls per hour.

## Accomplishments that we're proud of

We all worked with technologies that we were not familiar with, and so we were able to learn a lot while developing a functional prototype. We synthesised different forms of machine learning by integrating speech-to-text technology with sentiment analysis, which let us detect a person’s emotions just using their spoken words. We used both facial recognition and the aforementioned speech-to-sentiment-analysis to develop a holistic approach in interpreting a person’s emotions. We used Socket.IO to create real-time input and output data streams to maximize efficiency.

## What we learned

We learnt about web sockets, how to develop a web app using web sockets and how to debug web socket errors. We also learnt how to harness the power of Microsoft Azure's Machine Learning and Cognitive Science libraries. We learnt that "cat" has a positive sentiment analysis and "dog" has a neutral score, which makes no sense what so ever because dogs are definitely way cuter than cats. (Zarif strongly disagrees)

## What's next for SmartEQ

We would deploy our hack onto real Smart Glasses :D. This would allow us to deploy our tech in real life, first into small sample groups to figure out what works and what doesn't work, and after we smooth out the kinks, we could publish it as an added technology for Smart Glasses. This app would also be useful for people with social-emotional agnosia, a condition that may be caused by brain trauma that can cause them to be unable to process facial expressions.
In addition, this technology has many other cool applications! For example, we could make it into a widely used company app that company employees can integrate into their online meeting tools. This is especially valuable for HR managers, who can monitor their employees’ emotional well beings at work and be able to implement initiatives to help if their employees are not happy.
## 💡 Inspiration

We got inspiration from or back-end developer Minh. He mentioned that he was interested in the idea of an app that helped people record their positive progress and showcase their accomplishments there. This then led to our product/UX designer Jenny to think about what this app would target as a problem and what kind of solution would it offer. From our research, we came to the conclusion quantity over quality social media use resulted in people feeling less accomplished and more anxious. As a solution, we wanted to focus on an app that helps people stay focused on their own goals and accomplishments.

## ⚙ What it does

Our app is a journalling app that has the user enter 2 journal entries a day. One in the morning and one in the evening. During these journal entries, it would ask the user about their mood at the moment, generate am appropriate response based on their mood, and then ask questions that get the user to think about such as gratuity, their plans for the day, and what advice would they give themselves. Our questions follow many of the common journalling practices. The second journal entry then follows a similar format of mood and questions with a different set of questions to finish off the user's day. These help them reflect and look forward to the upcoming future. Our most powerful feature would be the AI that takes data such as emotions and keywords from answers and helps users generate journal summaries across weeks, months, and years. These summaries would then provide actionable steps the user could take to make self-improvements.

## 🔧 How we built it

### Product & UX

* Online research, user interviews, looked at stakeholders, competitors, infinity mapping, and user flows.
* Doing the research allowed our group to have a unified understanding for the app.

### 👩‍💻 Frontend

* Used React.JS to design the website
* Used Figma for prototyping the website

### 🔚 Backend

* Flask, CockroachDB, and Cohere for ChatAI function.

## 💪 Challenges we ran into

The challenge we ran into was the time limit. For this project, we invested most of our time in understanding the pinpoint in a very sensitive topic such as mental health and psychology. We truly want to identify and solve a meaningful challenge; we had to sacrifice some portions of the project such as front-end code implementation. Some team members were also working with the developers for the first time and it was a good learning experience for everyone to see how different roles come together and how we could improve for next time.

## 🙌 Accomplishments that we're proud of

Jenny, our team designer, did tons of research on problem space such as competitive analysis, research on similar products, and user interviews. We produced a high-fidelity prototype and were able to show the feasibility of the technology we built for this project. (Jenny: I am also very proud of everyone else who had the patience to listen to my views as a designer and be open-minded about what a final solution may look like. I think I'm very proud that we were able to build a good team together although the experience was relatively short over the weekend. I had personally never met the other two team members and the way we were able to have a vision together is something I think we should be proud of.)

## 📚 What we learned

We learned preparing some plans ahead of time next time would make it easier for developers and designers to get started. However, the experience of starting from nothing and making a full project over 2 and a half days was great for learning. We learned a lot about how we think and approach work not only as developers and designer, but as team members.

## 💭 What's next for budEjournal

Next, we would like to test out budEjournal on some real users and make adjustments based on our findings. We would also like to spend more time to build out the front-end.