## Inspiration

The three of us love lifting at the gym. We always see apps that track cardio fitness but haven't found anything that tracks lifting exercises in real-time. Often times when lifting, people tend to employ poor form leading to gym injuries which could have been avoided by being proactive.

## What it does and how we built it

Our product tracks body movements using EMG signals from a Myo armband the athlete wears. During the activity, the application provides real-time tracking of muscles used, distance specific body parts travel and information about the athlete’s posture and form. Using machine learning, we actively provide haptic feedback through the band to correct the athlete’s movements if our algorithm deems the form to be poor.

## How we built it

We trained an SVM based on employing deliberately performed proper and improper forms for exercises such as bicep curls. We read properties of the EMG signals from the Myo band and associated these with the good/poor form labels. Then, we dynamically read signals from the band during workouts and chart points in the plane where we classify their forms. If the form is bad, the band provides haptic feedback to the user indicating that they might injure themselves.

## Challenges we ran into

Interfacing with the Myo bands API was not the easiest task for us, since we ran into numerous technical difficulties. However, after we spent copious amounts of time debugging, we finally managed to get a clear stream of EMG data.

## Accomplishments that we're proud of

We made a working product by the end of the hackathon (including a fully functional machine learning model) and are extremely excited for its future applications.

## What we learned

It was our first time making a hardware hack so it was a really great experience playing around with the Myo and learning about how to interface with the hardware. We also learned a lot about signal processing.

## What's next for SpotMe

In addition to refining our algorithms and depth of insights we can provide, we definitely want to expand the breadth of activities we cover too (since we’re primarily focused on weight lifting too).

The market we want to target is sports enthusiasts who want to play like their idols. By collecting data from professional athletes, we can come up with “profiles” that the user can learn to play like. We can quantitatively and precisely assess how close the user is playing their chosen professional athlete.

For instance, we played tennis in high school and frequently had to watch videos of our favorite professionals. With this tool, you can actually learn to serve like Federer, shoot like Curry or throw a spiral like Brady.
## Inspiration

Members of our team know multiple people who suffer from permanent or partial paralysis. We wanted to build something that could be fun to develop and use, but at the same time make a real impact in people's everyday lives. We also wanted to make an affordable solution, as most solutions to paralysis cost thousands and are inaccessible. We wanted something that was modular, that we could 3rd print and also make open source for others to use.

## What it does and how we built it

The main component is a bionic hand assistant called The PulseGrip. We used an ECG sensor in order to detect electrical signals. When it detects your muscles are trying to close your hand it uses a servo motor in order to close your hand around an object (a foam baseball for example). If it stops detecting a signal (you're no longer trying to close) it will loosen your hand back to a natural resting position. Along with this at all times it sends a signal through websockets to our Amazon EC2 server and game. This is stored on a MongoDB database, using API requests we can communicate between our games, server and PostGrip. We can track live motor speed, angles, and if it's open or closed. Our website is a full-stack application (react styled with tailwind on the front end, node js on the backend). Our website also has games that communicate with the device to test the project and provide entertainment. We have one to test for continuous holding and another for rapid inputs, this could be used in recovery as well.

## Challenges we ran into

This project forced us to consider different avenues and work through difficulties. Our main problem was when we fried our EMG sensor, twice! This was a major setback since an EMG sensor was going to be the main detector for the project. We tried calling around the whole city but could not find a new one. We decided to switch paths and use an ECG sensor instead, this is designed for heartbeats but we managed to make it work. This involved wiring our project completely differently and using a very different algorithm. When we thought we were free, our websocket didn't work. We troubleshooted for an hour looking at the Wifi, the device itself and more. Without this, we couldn't send data from the PulseGrip to our server and games. We decided to ask for some mentor's help and reset the device completely, after using different libraries we managed to make it work. These experiences taught us to keep pushing even when we thought we were done, and taught us different ways to think about the same problem.

## Accomplishments that we're proud of

Firstly, just getting the device working was a huge achievement, as we had so many setbacks and times we thought the event was over for us. But we managed to keep going and got to the end, even if it wasn't exactly what we planned or expected. We are also proud of the breadth and depth of our project, we have a physical side with 3rd printed materials, sensors and complicated algorithms. But we also have a game side, with 2 (questionable original) games that can be used. But they are not just random games, but ones that test the user in 2 different ways that are critical to using the device. Short burst and hold term holding of objects. Lastly, we have a full-stack application that users can use to access the games and see live stats on the device.

## What's next for PulseGrip

* working to improve sensors, adding more games, seeing how we can help people

We think this project had a ton of potential and we can't wait to see what we can do with the ideas learned here.

## Check it out

<https://hacks.pulsegrip.design>

<https://github.com/PulseGrip>
## Inspiration

We got the inspiration by watching each other collapsing to finalize a hackathon idea at 5 a.m. in the morning. We've tried multiple other methods like hackathon idea generators online but didn't get much idea from those randomly arranged tech buzzwords. Then the divine inspiration came as we would like to develop a solution that solves our pain point on the spot.

## What it does

Our product provides users, and hackers in all hackathons, an opportunity to generate a unique, challenging, potentially award-winning idea in filtered areas of interest. We made sure that the concept is highly potential and outstanding in the fierce competition of hackathons by using the NLP algorithm powered by co:here API to analyze all winner projects on the Devpost website.

## How we built it

We powered up co:here API to read the input we scraped from past hackathon projects on Devpost. We fetch the information we need such as tools and descriptions. Then the output is shown in an interface designed by Figma and implemented by React.

## Challenges we ran into

We have to limit the input and output size as the sum of those has a maximum limit. Hence, we have to find a way to balance the number of projects, and the amount of info we feed into the algorithm. Also, lots of bugs when trying to connect the frontend and backend.

## Accomplishments that we're proud of

Everybody who heard the idea got a good laugh. Aside from the fun examples, we got a few project topics that sound interesting and doable in a hackathon environment based on feedback from multiple friends. We also managed to hook up the backend and frontend after endless debugging which allowed users to use the program without much learning effort.

## What we learned

We got our hands wet in the NLP and ML area during this project. We understood more about the true potential of artificial intelligence after seeing this task that we struggle with so much can be solved this way. And we got some cool hackathon ideas in our banks!

## What's next for New Project Title

We would like to add features to allow people multi-select more than one area of interest to serve better when the hackathon group doesn't have a clear mind about what topic to tackle. We also would like to train our own model using all the winner posts on the Devpost to get a better result.