## Inspiration

More than **2 million** people in the United States are affected by diseases such as ALS, brain or spinal cord injuries, cerebral palsy, muscular dystrophy, multiple sclerosis, and numerous other diseases that impair muscle control. Many of these people are confined to their wheelchairs, some may be lucky enough to be able to control their movement using a joystick. However, there are still many who cannot use a joystick, eye tracking systems, or head movement-based systems.

Therefore, a brain-controlled wheelchair can solve this issue and provide freedom of movement for individuals with physical disabilities.

## What it does

BrainChair is a neurally controlled headpiece that can control the movement of a motorized wheelchair. There is no using the attached joystick, just simply think of the wheelchair movement and the wheelchair does the rest!

## How we built it

The brain-controlled wheelchair allows the user to control a wheelchair solely using an OpenBCI headset. The headset is an Electroencephalography (EEG) device that allows us to read brain signal data that comes from neurons firing in our brain. When we think of specific movements we would like to do, those specific neurons in our brain will fire. We can collect this EEG data through the Brainflow API in Python, which easily allows us to stream, filter, preprocess the data, and then finally pass it into a classifier.

The control signal from the classifier is sent through WiFi to a Raspberry Pi which controls the movement of the wheelchair. In our case, since we didn’t have a motorized wheelchair on hand, we used an RC car as a replacement. We simply hacked together some transistors onto the remote which connects to the Raspberry Pi.

## Challenges we ran into

* Obtaining clean data for training the neural net took some time. We needed to apply signal processing methods to obtain the data
* Finding the RC car was difficult since most stores didn’t have it and were closed. Since the RC car was cheap, its components had to be adapted in order to place hardware pieces.
* Working remotely made designing and working together challenging. Each group member worked on independent sections.

## Accomplishments that we're proud of

The most rewarding aspect of the software is that all the components front the OpenBCI headset to the raspberry-pi were effectively communicating with each other

## What we learned

One of the most important lessons we learned is effectively communicating technical information to each other regarding our respective disciplines (computer science, mechatronics engineering, mechanical engineering, and electrical engineering).

## What's next for Brainchair

To improve BrainChair in future iterations we would like to:

Optimize the circuitry to use low power so that the battery lasts months instead of hours. We aim to make the OpenBCI headset not visible by camouflaging it under hair or clothing.
## Inspiration

We realized how visually-impaired people find it difficult to perceive the objects coming near to them, or when they are either out on road, or when they are inside a building. They encounter potholes and stairs and things get really hard for them. We decided to tackle the issue of accessibility to support the Government of Canada's initiative to make work and public places completely accessible!

## What it does

This is an IoT device which is designed to be something wearable or that can be attached to any visual aid being used. What it does is that it uses depth perception to perform obstacle detection, as well as integrates Google Assistant for outdoor navigation and all the other "smart activities" that the assistant can do. The assistant will provide voice directions (which can be geared towards using bluetooth devices easily) and the sensors will help in avoiding obstacles which helps in increasing self-awareness. Another beta-feature was to identify moving obstacles and play sounds so the person can recognize those moving objects (eg. barking sounds for a dog etc.)

## How we built it

Its a raspberry-pi based device and we integrated Google Cloud SDK to be able to use the vision API and the Assistant and all the other features offered by GCP. We have sensors for depth perception and buzzers to play alert sounds as well as camera and microphone.

## Challenges we ran into

It was hard for us to set up raspberry-pi, having had no background with it. We had to learn how to integrate cloud platforms with embedded systems and understand how micro controllers work, especially not being from the engineering background and 2 members being high school students. Also, multi-threading was a challenge for us in embedded architecture

## Accomplishments that we're proud of

After hours of grinding, we were able to get the raspberry-pi working, as well as implementing depth perception and location tracking using Google Assistant, as well as object recognition.

## What we learned

Working with hardware is tough, even though you could see what is happening, it was hard to interface software and hardware.

## What's next for i4Noi

We want to explore more ways where i4Noi can help make things more accessible for blind people. Since we already have Google Cloud integration, we could integrate our another feature where we play sounds of living obstacles so special care can be done, for example when a dog comes in front, we produce barking sounds to alert the person. We would also like to implement multi-threading for our two processes and make this device as wearable as possible, so it can make a difference to the lives of the people.
## ✨ Inspiration

When we began drafting ideas for this hackathon, our team really wanted to focus on tackling real-world problems. We wanted to create a platform that helped users fully realize the issues that our world was facing. After some deliberation, we decided to analyze and display climate change data. By visualizing the data in a simpler format, it allows users to get a better understanding of the areas in Canada that are affected by climate change.

## 🌎 What it does

Degrees of Change is a React app that elegantly displays climate change data in Canada. With its toggles and slider, users can effortlessly view climate data in Canada over the years. The app has a main landing page that gives a brief introduction, as well as some information on climate change, before directing them to the hands-on information display. As you move the slider, the user can see the global temperatures rising and falling on our 3-dimensional diagram, which is displayed on a moveable map of Canada.

## 🔨 How we built it

Our team drafted the designs for Degrees of Change using **Figma** and brought the front-end designs into reality through **React** and **Typescript**. We wanted to keep the front-end minimalistic to let the data speak for itself. For the backend, we used **SQLite** with **Flask** to analyze the climate change dataset provided by the [Government of Canada](https://climate-change.canada.ca/climate-data/#/adjusted-station-data). The data was then presented using **Deck.gl** to bring a simple map of Canada to life.

## 🥊 Challenges we ran into

Although our team has experienced a few hackathons in the past, we still faced some unique challenges during Hack the 6ix. For our team, it found it challenging as it was our first time taking on a full-stack role during this hackathon. We explored unfamiliar frameworks to complete this project. Although we faced many challenges with the frameworks, it was ultimately incredibly rewarding to see your hard work pay off into a fully-fledged app.

## 🏆 Accomplishments that we're proud of

Overall our team is quite proud of the work that we have done in this hackathon. We were able to successfully learn and apply new applications such as Deck.gl. Learning and using these new tools allowed us to create a better visual component of our project, which helped to elevate the purpose of our project, which is to allow users to explore climate change data.

## 🧠 What we learned

As mentioned before, we stepped outside of our comfort zone and decided to tackle some unfamiliar frameworks for our hackathon project. Some of our members have not worked with full stack projects before. We are all very proud of how far we’ve come and how much we have learned during this hackathon. Each of us will walk away having learned something new while also being eager to incorporate it into our next project. This hackathon proved to be an immense learning experience as we gained insights into future tools that can be used to develop future projects.

## 🚀 What's next for Degrees of Change

Given more time, we hope to expand the scope of the app to cover more countries, ultimately leading to an app that provides information on climate change around the entire world. One of the first steps would be to gather more datasets to provide information outside of Canada. We could also add more functionality to the user interface, to provide a more enriching experience for the user as we provide a more in-depth look into the statistics. Another future direction includes adding in different data science techniques. Through the analysis of the time-series data along with additional information such as current global activities (carbon emissions), we could build a predictive model to project for future temperatures.
In addition setting up an interface for business/researchers to submit there own climate data and to predict future conditions given a set of changeable parameters would create a viable business product to sold as a service.