## Inspiration

Physiotherapy is expensive for what it provides you with, A therapist stepping you through simple exercises and giving feedback and evaluation? WE CAN TOTALLY AUTOMATE THAT! We are undergoing the 4th industrial revolution and technology exists to help people in need of medical aid despite not having the time and money to see a real therapist every week.

## What it does

IMU and muscle sensors strapped onto the arm accurately track the state of the patient's arm as they are performing simple arm exercises for recovery. A 3d interactive GUI is set up to direct patients to move their arm from one location to another by performing localization using IMU data. A classifier is run on this variable-length data stream to determine the status of the patient and how well the patient is recovering. This whole process can be initialized with the touch of a button on your very own mobile application.

## How WE built it

on the embedded system side of things, we used a single raspberry pi for all the sensor processing. The Pi is in charge of interfacing with the IMU while another Arduino interfaces with the other IMU and a muscle sensor. The Arduino then relays this info over a bridged connection to a central processing device where it displays the 3D interactive GUI and processes the ML data. all the data in the backend is relayed and managed using ROS. This data is then uploaded to firebase where the information is saved on the cloud and can be accessed anytime by a smartphone. The firebase also handles plotting data to give accurate numerical feedback of the many values orientation trajectory, and improvement over time.

## Challenges WE ran into

hooking up 2 IMU to the same rpy is very difficult. We attempted to create a multiplexer system with little luck.
To run the second IMU we had to hook it up to the Arduino. Setting up the library was also difficult.
Another challenge we ran into was creating training data that was general enough and creating a preprocessing script that was able to overcome the variable size input data issue.
The last one was setting up a firebase connection with the app that supported the high data volume that we were able to send over and to create a graphing mechanism that is meaningful.
## Inspiration

A deep and unreasonable love of xylophones

## What it does

An air xylophone right in your browser!
Play such classic songs as twinkle twinkle little star, ba ba rainbow sheep and the alphabet song or come up with the next club banger in free play.

We also added an air guitar mode where you can play any classic 4 chord song such as Wonderwall

## How we built it

We built a static website using React which utilised Posenet from TensorflowJS to track the users hand positions and translate these to specific xylophone keys.

We then extended this by creating Xylophone Hero, a fun game that lets you play your favourite tunes without requiring any physical instruments.

## Challenges we ran into

Fine tuning the machine learning model to provide a good balance of speed and accuracy

## Accomplishments that we're proud of

I can get 100% on Never Gonna Give You Up on XylophoneHero (I've practised since the video)

## What we learned

We learnt about fine tuning neural nets to achieve maximum performance for real time rendering in the browser.

## What's next for XylophoneHero

We would like to:

* Add further instruments including a ~~guitar~~ and drum set in both freeplay and hero modes
* Allow for dynamic tuning of Posenet based on individual hardware configurations
* Add new and exciting songs to Xylophone
* Add a multiplayer jam mode
# Inspiration 🌟

**What is the problem?**
Physical activity early on can drastically increase longevity and productivity for later stages of life. Without finding a dependable routine during your younger years, you may experience physical impairment in the future. 50% of functional decline that occurs in those 30 to 70 years old is due to lack of exercise.

During the peak of the COVID-19 pandemic in Canada, nationwide isolation brought everyone indoors. There was still a vast number of people that managed to work out in their homes, which motivated us to create an application that further encouraged engaging in fitness, using their devices, from the convenience of their homes.

# Webapp Summary 📜

Inspired, our team decided to tackle this idea by creating a web app that helps its users maintain a consistent and disciplined routine.

# What does it do? 💻

*my trAIner* plans to aid you and your journey to healthy fitness by displaying the number of calories you have burned while also counting your reps. It additionally helps to motivate you through words of encouragement. For example, whenever nearing a rep goal, *my trAIner* will use phrases like, “almost there!” or “keep going!” to push you to the last rep. Once completing your set goal *my trAIner* will congratulate you.

We hope that people may utilize this to make the best of their workouts. Utilizing AI technology to help those reach their rep standard and track calories, we believe could help students and adults in the present and future.

# How we built it:🛠

To build this application, we used **JavaScript, CSS,** and **HTML.** To make the body mapping technology, we used a **TensorFlow** library. We mapped out different joints on the body and compared them as they moved, in order to determine when an exercise was completed. We also included features like parallax scrolling and sound effects from DeltaHacks staff.

# Challenges that we ran into 🚫

Learning how to use **TensorFlow**’s pose detection proved to be a challenge, as well as integrating our own artwork into the parallax scrolling. We also had to refine our backend as the library’s detection was shaky at times. Additional challenges included cleanly linking **HTML, JS, and CSS** as well as managing the short amount of time we were given.

# Accomplishments that we’re proud of 🎊

We are proud that we put out a product with great visual aesthetics as well as a refined detection method. We’re also proud that we were able to take a difficult idea and prove to ourselves that we were capable of creating this project in a short amount of time. More than that though, we are most proud that we could make a web app that could help out people trying to be more healthy.

# What we learned 🍎

Not only did we develop our technical skills like web development and AI, but we also learned crucial things about planning, dividing work, and time management. We learned the importance of keeping organized with things like to-do lists and constantly communicating to see what each other’s limitations and abilities were. When challenges arose, we weren't afraid to delve into unknown territories.

# Future plans 📅

Due to time constraints, we were not able to completely actualize our ideas, however, we will continue growing and raising efficiency by giving ourselves more time to work on *my trAIner*. Potential future ideas to incorporate may include constructive form correction, calorie intake calculator, meal preps, goal setting, recommended workouts based on BMI, and much more. We hope to keep on learning and applying newly obtained concepts to *my trAIner*.