## Inspiration

Alzheimer's is a very interesting, scary disease. There's no decided cause for the neuro-degenerative disease among scientists, and neither is there any cure. And on a personal level, we've all met people before who have had family members either currently or previously suffering from Alzheimer's. Inspired by this and the health challenges posted at this hackathon, we decided to come up with a tool to help with Alzheimer's diagnosis and recovery efforts.

## What it does

MindTune is a tool to detect early signs of Alzheimer's and other neuro-degenerative diseases in patients. At-risk patients download the app on their phone and participate in an in-app session on a weekly or daily basis. This session has 2 main features: a comprehensive Neuro-Cognition testing system and an accurate Eye-Tracking system. Doctors can then view this data in a separate website and analyze their patients for any early signs of diseases. Our Neuro-Cognition system works by prompting users questions about their life/day and then using NLP to analyze their responses for any warning signs of reduced cognitive ability. Our Eye-Tracking system uses a highly-accurate gaze-estimation model to check "jiteriness" in users' pupils as another warning sign of Alzheimer's.

## How we built it

MindTune was built with a lot of different tools, primarily:

* React Native - frontend development and user interactivity
* OpenCV - pupil-detection and movement tracking
* PyTorch - run inference with model
* Modal Labs - hosting and deploying
* FastAPI/Python - backend server, LLM integration, NLP

## Challenges we ran into

One of the major challenges we ran into was getting eye-tracking to work in real-time. At first, we developed our own pupil-recognition system with OpenCV. However, this system was slow and couldn't be effectively used in the app. We moved over to a new model (gaze-estimation) and invested heavy effort into making it run as fast as possible with low latency. Another challenge was designing the neuro-cognition system. The questions our system asked had to be relevant to users while still staying semi-randomized.

## Accomplishments that we're proud of

We're really proud of our eye-tracking system. We researched links between pupil movements and Alzheimer's to find the best method of detecting warning signs through pupil jitteriness. We're also proud of our accessible UI, meant to be easy to use by elderly patients, caretakers, and doctors.

## What we learned

We learned a lot about facial recognition and utilizing OpenCV for such recognition tasks. We also learned about using React Native for front-end app development.

## What's next for MindTune

In the future, we want to add even more cognitive tests and puzzles to help find more warning signs in users. We also want to expand the doctor portal to have more advanced analysis of patient data.
## Inspiration

We can make customer support so, so much better, for both customers and organizations alike. This project was inspired by the frankly terrible wait times and customer support information that the DriveTest centres across Ontario have.

## What it does

From a high-level, our platform integrates analytics of both in-person and online customer support lines (using computer vision and Genesys's API, respectively), and uses those to provide customers real-time data for which customer support channel to utilize at a given time. It also provides organizations with analytics and metrics to be able to optimize their customer support pipelines.

Speaking about the internals, our platform utilizes computer vision to determine the number of people in a line at any given moment, then uses AI to calculate the approximate waiting time for that line. This usecase is meant for in-person customer support interactions. Our platform also uses Genesys's Developer API (EstimateWaitTime) to calculate, for any given organization and queue, the wait time and backlog of customer support cases. It then combines these two forms of customer support, allowing customers to make informed decisions as to where to go for customer support, and giving organizations robust analytics for which customer support channels can be further optimized (such as hiring more people to serve as chat agents).

## How we built it

OpenCV along with a custom algorithm for people counting within a certain bounding area was used for the Computer Vision aspect to determine the number of people in a line, in-person. This data is sent to a Flask server. We also used Genesys's API along with simulated Genesys customer-agent interactions to determine how long the wait time is for online customer support. From the Flask server, this data goes to two different front-ends:

1. For customers: customers simply see a dashboard with the wait time for online customer support, as well as the wait times at nearby branches of the company (say, Ontario DriveTest centres) – created using Bulma and Vanilla JS
2. For organizations: organizations see robust analytics regarding wait times at certain intervals, certain points in the day, etc. They can also compare and contrast online and in-person customer support analytics. Organizations can use these metrics to optimize customer support to reduce the load on certain employees, and by making customer support more efficient for customers.

## Challenges we ran into

Working with many services (Computer Vision + Python, Flask backend, Vanilla JS frontend, Vue.js frontend) was a challenge, since we had to find a way to pass the data from one service to another, reliably. We decided to fix this by using a key-value store for redundancy to ensure data is not lost through numerous layers of transmission.

## Accomplishments that we're proud of

Creating a working product using Genesys's API!

## What we learned

The opportunity that lies within the field of customer support and unifying both online and in-person components of it. Also, the opportunities that Genesys's API holds in terms of empowering organizations to make their customer support as efficient as possible.

## What's next for QuicQ

We wanted to use infrared sensors instead of cameras to detect people in a line in-person, due to privacy concerns, but we couldn't find infrared sensors for this hackathon! So, we will integrate them in a future version of QuicQ.
## 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.