## What it does

XEN SPACE is an interactive web-based game that incorporates emotion recognition technology and the Leap motion controller to create an immersive emotional experience that will pave the way for the future gaming industry.

# How we built it

We built it using three.js, Leap Motion Controller for controls, and Indico Facial Emotion API. We also used Blender, Cinema4D, Adobe Photoshop, and Sketch for all graphical assets.
## Inspiration

Having previously volunteered and worked with children with cerebral palsy, we were struck with the monotony and inaccessibility of traditional physiotherapy. We came up with a cheaper, more portable, and more engaging way to deliver treatment by creating virtual reality games geared towards 12-15 year olds. We targeted this age group because puberty is a crucial period for retention of plasticity in a child's limbs. We implemented interactive games in VR using Oculus' Rift and Leap motion's controllers.

## What it does

We designed games that targeted specific hand/elbow/shoulder gestures and used a leap motion controller to track the gestures. Our system improves motor skill, cognitive abilities, emotional growth and social skills of children affected by cerebral palsy.

## How we built it

Our games use of leap-motion's hand-tracking technology and the Oculus' immersive system to deliver engaging, exciting, physiotherapy sessions that patients will look forward to playing. These games were created using Unity and C#, and could be played using an Oculus Rift with a Leap Motion controller mounted on top. We also used an Alienware computer with a dedicated graphics card to run the Oculus.

## Challenges we ran into

The biggest challenge we ran into was getting the Oculus running. None of our computers had the ports and the capabilities needed to run the Oculus because it needed so much power. Thankfully we were able to acquire an appropriate laptop through MLH, but the Alienware computer we got was locked out of windows. We then spent the first 6 hours re-installing windows and repairing the laptop, which was a challenge. We also faced difficulties programming the interactions between the hands and the objects in the games because it was our first time creating a VR game using Unity, leap motion controls, and Oculus Rift.

## Accomplishments that we're proud of

We were proud of our end result because it was our first time creating a VR game with an Oculus Rift and we were amazed by the user experience we were able to provide. Our games were really fun to play! It was intensely gratifying to see our games working, and to know that it would be able to help others!

## What we learned

This project gave us the opportunity to educate ourselves on the realities of not being able-bodied. We developed an appreciation for the struggles people living with cerebral palsy face, and also learned a lot of Unity.

## What's next for Alternative Physical Treatment

We will develop more advanced games involving a greater combination of hand and elbow gestures, and hopefully get testing in local rehabilitation hospitals. We also hope to integrate data recording and playback functions for treatment analysis.

## Business Model Canvas

<https://mcgill-my.sharepoint.com/:b:/g/personal/ion_banaru_mail_mcgill_ca/EYvNcH-mRI1Eo9bQFMoVu5sB7iIn1o7RXM_SoTUFdsPEdw?e=SWf6PO>
## Inspiration

We spend a lot of our time sitting in front of the computer. The idea is to use the video feed from webcam to determine the emotional state of the user, analyze and provide a feedback accordingly in the form of music, pictures and videos.

## How I built it

Using Microsoft Cognitive Services (Video + Emotion API) we get the emotional state of the user through the webcam feed. We parse that to the bot framework which in turn sends responses based upon the change in the values of emotional state.

## Challenges I ran into

Passing data between the bot framework and the desktop application which captured the webcam feed.

## Accomplishments that I'm proud of

A fully functional bot which provides feedback to the user based upon the changes in the emotion.

## What I learned

Visual studio is a pain to work with.

## What's next for ICare

Use Recurrent Neural Network to keep track of the emotional state of the user before and after and improve the content provided to the user over the period of time.