## Inspiration

Feeling major self-doubt when you first start hitting the gym or injuring yourself accidentally while working out are not uncommon experiences for most people. This inspired us to create Core, a platform to empower our users to take control of their well-being by removing the financial barriers around fitness.

## What it does

Core analyses the movements performed by the user and provides live auditory feedback on their form, allowing them to stay fully present and engaged during their workout. Our users can also take advantage of the visual indications on the screen where they can view a graph of the keypoint which can be used to reduce the risk of potential injury.

## How we built it

Prior to development, a prototype was created on Figma which was used as a reference point when the app was developed in ReactJs. In order to recognize the joints of the user and perform analysis, Tensorflow's MoveNet model was integrated into Core.

## Challenges we ran into

Initially, it was planned that Core would serve as a mobile application built using React Native, but as we developed a better understanding of the structure, we saw more potential in a cross-platform website. Our team was relatively inexperienced with the technologies that were used, which meant learning had to be done in parallel with the development.

## Accomplishments that we're proud of

This hackathon allowed us to develop code in ReactJs, and we hope that our learnings can be applied to our future endeavours. Most of us were also new to hackathons, and it was really rewarding to see how much we accomplished throughout the weekend.

## What we learned

We gained a better understanding of the technologies used and learned how to develop for the fast-paced nature of hackathons.

## What's next for Core

Currently, Core uses TensorFlow to track several key points and analyzes the information with mathematical models to determine the statistical probability of the correctness of the user's form. However, there's scope for improvement by implementing a machine learning model that is trained on Big Data to yield higher performance and accuracy.
We'd also love to expand our collection of exercises to include a wider variety of possible workouts.
## Inspiration

To spread the joy of swag collecting.

## What it does

A Hack the North Simulator, specifically of the Sponsor Bay on the second floor. The player will explore sponsor booths, collecting endless amounts of swag along the way. A lucky hacker may stumble upon the elusive, exclusive COCKROACH TROPHY, or a very special RBC GOLD PRIZE!

## How we built it

Unity, Aseprite, Cakewalk

## What we learned

We learned the basics of Unity and git, rigidbody physics, integrating audio into Unity, and the creation of game assets.
## Inspiration:

The need for an accessible workout tool that helps improve form and keep users engaged.

## What it does:

Gives real-time feedback on the user's form during workouts.

## How we built it

Tracking and Evaluation: This was coded in javascript and built using html. It incorporates a trained TensorFlow model call PoseNet to receive live data on "keypoints" in a video stream. The key points correspond to joints in the user's body. The keypoints motion and relative position is then used to evaluate the user's form.

## Challenges we ran into

Integrating machine learning with computer vision isn't simple, even when trying to use a pre-trained model. Some similar technologies are even more complex or require massive technology requirements (equivalent to ~$2,400 video card) so finding the correct model and platform for our application was critical and challenging.

## Accomplishments that we're proud of

This being the first hackathon for every member of the team, we are very proud of the learning we all achieved and the final product we were able to create. We learned so much about coding languages we were unfamiliar with (some members learned new languages from scratch), computer vision, machine learning, data models, and mobile UI/UX design. With our limited coding experience, we were able to research and persist through learning barriers and finish with something to show for it.

## What we learned

We learned how to track body movements using PoseNet and TensorFlow with Javascript, how to effectively use virtual environments to run the program locally, and how to communicate with the user through UI/UX on mobile devices.

## What's next for Trackout

There is a lot of potential for TrackOut to become a huge platform to host an amazing community of users wanting to improve their workout routine. By using recurring neural networks, TrackOut will be able to provide specific and meaningful feedback to help the user achieve high levels of form and consistency with their workouts. TrackOut will also have an extensive social aspect, connecting users by allowing them to share their own workouts and help each other by providing feedback in comment sections. Finally, Trackout seeks to collaborate with major YouTube and Instagram influencers within the existing online workout space, to bring a high volume of users, and to keep them actively involed in the community.