## Inspiration

Neuro-Matter is an integrated social platform designed to combat not one, but 3 major issues facing our world today: Inequality, Neurological Disorders, and lack of information/news.

We started Neuro-Matter with the aim of helping people facing the issue of Inequality at different levels of society. Though it was assumed that inequality only leads to physical violence, its impacts on neurological/mental levels are left neglected.

Upon seeing the disastrous effects, we have realized the need of this hour and have come up with Neuro-Matter to effectively combat these issues in addition to the most pressing issue our world faces today: mental health!

## What it does

1. "Promotes Equality" and provides people the opportunity to get out of mental trauma.
2. Provides a hate-free social environment.
3. Helps People cure the neurological disorder
4. Provide individual guidance to support people with the help of our volunteers.
5. Provides reliable news/information.
6. Have an AI smart chatbot to assist you 24\*7.

## How we built it

Overall, we used HTML, CSS, React.js, google cloud, dialogue flow, google maps Twilio's APIs. We used Google Firebase's Realtime Database to store, organize, and secure our user data. This data is used to login and sign up for the service. The service's backend is made with node.js, which is used to serve the webpages and enable many useful functions. We have multiple different pages as well like the home page, profile page, signup/login pages, and news/information/thought sharing page.

## Challenges we ran into

We had a couple of issues with databasing as the password authentication would work sometimes. Moreover, since we used Visual Studio multiplayer for the first time it was difficult as we faced many VSCode issues (not code related). Since we were working in the same time zones, it was not so difficult for all of us to work together, but It was hard to get everything done on time and have a rigid working module.

## Accomplishments that we're proud of

Overall, we are proud to create a working social platform like this and are hopeful to take it to the next steps in the future as well. Specifically, each of our members is proud of their amazing contributions.
We believe in the module we have developed and are determined to take this forward even beyond the hackathon to help people in real life.

## What we learned

We learned a lot, to say the least!! Overall, we learned a lot about databasing and were able to strengthen our React.js, Machine Learning, HTML, and CSS skills as well. We successfully incorporated Twilio's APIs and were able to pivot and send messages. We have developed a smart bot that is capable of both text and voice-based interaction. Overall, this was an extremely new experience for all of us and we greatly enjoyed learning new things. This was a great project to learn more about platform development.

## What's next for Neuro-Matter

This was an exciting new experience for all of us and we're all super passionate about this platform and can't wait to hopefully unveil it to the general public to help people everywhere by solving the issue of Inequality.
## Inspiration

Given the increase in mental health awareness, we wanted to focus on therapy treatment tools in order to enhance the effectiveness of therapy. Therapists rely on hand-written notes and personal memory to progress emotionally with their clients, and there is no assistive digital tool for therapists to keep track of clients’ sentiment throughout a session. Therefore, we want to equip therapists with the ability to better analyze raw data, and track patient progress over time.

## Our Team

* Vanessa Seto, Systems Design Engineering at the University of Waterloo
* Daniel Wang, CS at the University of Toronto
* Quinnan Gill, Computer Engineering at the University of Pittsburgh
* Sanchit Batra, CS at the University of Buffalo

## What it does

Inkblot is a digital tool to give therapists a second opinion, by performing sentimental analysis on a patient throughout a therapy session. It keeps track of client progress as they attend more therapy sessions, and gives therapists useful data points that aren't usually captured in typical hand-written notes.

Some key features include the ability to scrub across the entire therapy session, allowing the therapist to read the transcript, and look at specific key words associated with certain emotions. Another key feature is the progress tab, that displays past therapy sessions with easy to interpret sentiment data visualizations, to allow therapists to see the overall ups and downs in a patient's visits.

## How we built it

We built the front end using Angular and hosted the web page locally. Given a complex data set, we wanted to present our application in a simple and user-friendly manner. We created a styling and branding template for the application and designed the UI from scratch.

For the back-end we hosted a REST API built using Flask on GCP in order to easily access API's offered by GCP.
Most notably, we took advantage of Google Vision API to perform sentiment analysis and used their speech to text API to transcribe a patient's therapy session.

## Challenges we ran into

* Integrated a chart library in Angular that met our project’s complex data needs
* Working with raw data
* Audio processing and conversions for session video clips

## Accomplishments that we're proud of

* Using GCP in its full effectiveness for our use case, including technologies like Google Cloud Storage, Google Compute VM, Google Cloud Firewall / LoadBalancer, as well as both Vision API and Speech-To-Text
* Implementing the entire front-end from scratch in Angular, with the integration of real-time data
* Great UI Design :)

## What's next for Inkblot

* Database integration: Keeping user data, keeping historical data, user profiles (login)
* Twilio Integration
* HIPAA Compliancy
* Investigate blockchain technology with the help of BlockStack
* Testing the product with professional therapists
## Inspiration

After playing laser tag as kids and recently rediscovering it as adults but not having any locations near us to play, our team looked for a solution that wasn't tied down to a physical location. We wanted to be able to play laser tag anywhere, anytime!

## What it does

Quick Connect Laser Tag allows for up to 20 players to pick up one of our custom-made laser blaster and sensor device, and allows players to engage in a fun game of laser tag in 2 different game modes!

Game Mode 1: Last One Standing
In Last One Standing, all players have 10 lives. The last player with remaining lives is the winner

Game Mode 2: Time Deathmatch
In Time Deathmatch. A game duration is set, and the player with the most eliminations at the end of the game is the winner.

## How we built it

We 3D printed a housing for the laser blaster which contains an ESP32, laser transmitter, and laser sensor. We designed the laser blasters to communicate with each other during gameplay over ESP32Now peer-to-peer communication to relay point scoring data. When the laser sensor detects a hit, it can send an acknowledgement to the blaster that made the hit in order to correctly assign points to players. We worked with an OLED display over I2C to show the user their remaining lives or points scored and game time remaining, depending on the game mode.

## Challenges we ran into

To implement game mode two, we initially tried to use the HDK Android development board to act as a server that could communicate with the individual laser guns to tally point totals, however we were unable to get the board to work with WiFi over the eduroam network, or enable Direct WiFi to the laser blasters.

## Accomplishments that we're proud of

We're proud that when we were unable to work with the HDK Android development board, we were able to pivot to a different technology that could enable us to get our blasters to communicate. We'd never used the ESPNow communication protocol, and successfully used it to connect a theoretical limit of 20 blaster over a field range of 200 yards!

## What we learned

* ESPNow using MAC addresses
	+ Asynchronously receiving packets
* 3D Printing
* Working with OLED displays
* Using lasers to trigger sensors over long distances

## What's next for Quick Connect Laser Tag

* Building more blasters to support more players
* Custom PCB
* Improving blaster housing