## Inspiration

One of our own member's worry about his puppy inspired us to create this project, so he could keep an eye on him.

## What it does

Our app essentially monitors your dog(s) and determines their mood/emotional state based on their sound and body language, and optionally notifies the owner about any changes in it. Specifically, if the dog becomes agitated for any reasons, manages to escape wherever they are supposed to be, or if they fall asleep or wake up.

## How we built it

We built the behavioral detection using OpenCV and TensorFlow with a publicly available neural network. The notification system utilizes the Twilio API to notify owners via SMS. The app's user interface was created using JavaScript, CSS, and HTML.

## Challenges we ran into

We found it difficult to identify the emotional state of the dog using only a camera feed. Designing and writing a clean and efficient UI that worked with both desktop and mobile platforms was also challenging.

## Accomplishments that we're proud of

Our largest achievement was determining whether the dog was agitated, sleeping, or had just escaped using computer vision. We are also very proud of our UI design.

## What we learned

We learned some more about utilizing computer vision and neural networks.

## What's next for PupTrack

KittyTrack, possibly
Improving the detection, so it is more useful for our team member
## Inspiration

There are two types of pets wandering unsupervised in the streets - ones that are lost and ones that don't have a home to go to. Pet's Palace portable mini-shelters services these animals and connects them to necessary services while leveraging the power of IoT.

## What it does

The units are placed in streets and alleyways. As an animal approaches the unit, an ultrasonic sensor triggers the door to open and dispenses a pellet of food. Once inside, a live stream of the interior of the unit is sent to local animal shelters which they can then analyze and dispatch representatives accordingly. Backend analysis of the footage provides information on breed identification, custom trained data, and uploads an image to a lost and found database. A chatbot is implemented between the unit and a responder at the animal shelter.

## How we built it

Several Arduino microcontrollers distribute the hardware tasks within the unit with the aide of a wifi chip coded in Python. IBM Watson powers machine learning analysis of the video content generated in the interior of the unit. The adoption agency views the live stream and related data from a web interface coded with Javascript.

## Challenges we ran into

Integrating the various technologies/endpoints with one Firebase backend.

## Accomplishments that we're proud of

A fully functional prototype!
## Inspiration

We were inspired by popular language-learning platforms like Duolingo and wanted to explore how an unconventional form of negative reinforcement could impact the learning process. The idea of introducing mild physical stimuli, such as a shock, stemmed from a curiosity about how different motivators can influence human behavior and retention.

## What it does

This app takes the standard question-and-answer format of language learning to the next level by incorporating a shock mechanism. If a user answers a question incorrectly, they receive a small shock, creating an intense reinforcement mechanism intended to encourage greater focus and memory retention.

## How we built it

We used React, Next.js for the front end. The hardware component involves a dog shock collar that delivers the shock, controlled via a Flipper Zero for signal transmission with the app. We utilized the OpenAI API to generate questions, options and answers and used a simple API to communicate between the learning platform and the wristband, synchronizing the user's responses with the feedback.

## Challenges we ran into

One of the main challenges was ensuring the shock intensity was both safe and effective. Finding a balance between an attention-getting stimulus and something that was not overly painful was tricky.

## Accomplishments that we're proud of

We're proud of creating a fully functional prototype that integrates hardware with software, something that required interdisciplinary work between software engineering and hardware design. We are also proud our frontend with 3D rendering

## What we learned

We learned a lot about the psychology of learning, specifically how negative reinforcement can be both a motivator and a deterrent. On the technical side, we improved our skills in hardware-software integration, ensuring user safety when dealing with physical stimuli.

## What's next for Brainstorm

For the future, we plan to conduct research studies to better understand the effects of this learning method. We also want to refine the hardware to make it more comfortable and potentially less intimidating. Additionally, we are exploring other forms of feedback, to provide users with a variety of reinforcement options.