## Inspiration

1. Affordable pet doors with simple "flap" mechanisms are not secure
2. Potty trained pets requires the door to be manually opened (e.g. ring a bell, scratch the door)

## What it does

The puppy *(or cat, we don't discriminate)* can exit without approval as soon as the sensor detects an object within the threshold distance. When entering back in, the ultrasonic sensor will trigger a signal that something is at the door and the camera will take a picture and send to the owner's phone through a web app. The owner may approve or deny the request depending on the photo. If the owner approves the request, the door will open automatically.

## How we built it

Ultrasonic sensors relay the distance from the sensor to an object to the Arduino, which sends this signal to Raspberry Pi. The Raspberry Pi program handles the stepper motor movement (rotate ~90 degrees CW and CCW) to open and close the door and relays information to the Flask server to take a picture using the Kinect camera. This photo will display on the web application, where an approval to the request will open the door.

## Challenges we ran into

1. Connecting everything together (Arduino, Raspberry Pi, frontend, backend, Kinect camera) despite each component working well individually
2. Building cardboard prototype with limited resources = lots of tape & poor wire management
3. Using multiple different streams of I/O and interfacing with each concurrently

## Accomplishments that we're proud of

This was super rewarding as it was our first hardware hack! The majority of our challenges lie in the camera component as we're unfamiliar with Kinect but we came up with a hack-y solution and nothing had to be hardcoded.

## What we learned

Hardware projects require a lot of troubleshooting because the sensors will sometimes interfere with eachother or the signals are not processed properly when there is too much noise. Additionally, with multiple different pieces of hardware, we learned how to connect all the subsystems together and interact with the software components.

## What's next for PetAlert

1. Better & more consistent photo quality
2. Improve frontend notification system (consider push notifications)
3. Customize 3D prints to secure components
4. Use thermal instead of ultrasound
5. Add sound detection
## Inspiration

We as a team shared the same interest in knowing more about Machine Learning and its applications. upon looking at the challenges available, we were immediately drawn to the innovation factory and their challenges, and thought of potential projects revolving around that category. We started brainstorming, and went through over a dozen design ideas as to how to implement a solution related to smart cities. By looking at the different information received from the camera data, we landed on the idea of requiring the raw footage itself and using it to look for what we would call a distress signal, in case anyone felt unsafe in their current area.

## What it does

We have set up a signal that if done in front of the camera, a machine learning algorithm would be able to detect the signal and notify authorities that maybe they should check out this location, for the possibility of catching a potentially suspicious suspect or even being present to keep civilians safe.

## How we built it

First, we collected data off the innovation factory API, and inspected the code carefully to get to know what each part does. After putting pieces together, we were able to extract a video footage of the nearest camera to us. A member of our team ventured off in search of the camera itself to collect different kinds of poses to later be used in training our machine learning module. Eventually, due to compiling issues, we had to scrap the training algorithm we made and went for a similarly pre-trained algorithm to accomplish the basics of our project.

## Challenges we ran into

Using the Innovation Factory API, the fact that the cameras are located very far away, the machine learning algorithms unfortunately being an older version and would not compile with our code, and finally the frame rate on the playback of the footage when running the algorithm through it.

## Accomplishments that we are proud of

Ari: Being able to go above and beyond what I learned in school to create a cool project

Donya: Getting to know the basics of how machine learning works

Alok: How to deal with unexpected challenges and look at it as a positive change

Sudhanshu: The interesting scenario of posing in front of a camera while being directed by people recording me from a mile away.

## What I learned

Machine learning basics, Postman, working on different ways to maximize playback time on the footage, and many more major and/or minor things we were able to accomplish this hackathon all with either none or incomplete information.

## What's next for Smart City SOS

hopefully working with innovation factory to grow our project as well as inspiring individuals with similar passion or desire to create a change.
## Inspiration

Our inspiration came from a shared interest in sports and the unfortunate realization that-- getting better was getting harder! Private Coaching was getting more expensive, and self-coaching was difficult and time-consuming. To learn how to shoot with proper basketball shooting form, an athlete would have to be willing to pay inflated prices for lessons or be left to record themselves, play, analyze the film, and scour the internet to get better. We realized that we needed a coach; we needed an AI-powered coach to make our jobs as athletes much more efficient. Inspired by the hit film *Coach Carter (2008)* and the Bostonian accent, we developed "Coach Cahtah"!

## What it does

Coach Cahtah takes a video input of a player shooting a basketball anywhere on the court and uses Machine Learning to track and trace the ball's path to the hoop so that the user can better understand how their shooting form affects their field goal percentage.

## How we built it

We used Python for the backend of the project and the public Machine Learning library, OpenCV, to track the path of the basketball in the video. Instead of using hours of nonexistent basketball training data, we used a color-mapping method on the basketball to isolate its motion and cut our time to production exponentially.

## Challenges we ran into

First off, we needed to realize how difficult image recognition was. We did not have enough workforce or existing data to fulfill our intended goals, so instead, we simplified our ambitions to reach an MVP. We also attempted to use the Terra API with the Apple Watch, but due to its closed-system nature, it wasn't easy to pull gyroscopic data from the device.

## Accomplishments that we're proud of

Fortunately enough, we were able to adapt very well considering the situation. Instead of using TensorFlow, we ended up using OpenCV, which was more beginner-friendly, and we were able to circumvent the hours of training data we needed to train a model by taking a shortcut through the whole process. We used color mapping to isolate the basketball to trace and view its path to the hoop instead.

## What we learned

Machine learning has INCREDIBLE opportunities to make our daily lives INCREDIBLY efficient. The problem is that it requires a strong understanding and high-quality training data. With time and effort, using this tool can create unique products.

## What's next for Coach Cahtah

Coach Cahtah's greatest asset is its expandability. We can integrate all of these programs into a single app to allow athletes to analyze their shooting on the court efficiently. We can also move into other sports like Tennis.