## Inspiration

-Thought about accessibility problems that can occur in daily life.
-Regarding travel, lack of details and support from Google Maps.
-Friend decided not to come to MIT due to the lack of support for those with disabilities.

## What it does

-Determines the ideal paths to class for those requiring accessibility on the MIT Campus.
-Simple concept but is useful and can be implemented at various scales.

## How we built it

-Used MIT floor plan to place nodes at intersections and approximated distances between nodes.
-Defined elevators and stairs as edges, with stairs taking a third of the time as elevators (supported by empirical evidence).
-Used Dijkstra’s shortest path algorithm
-Wrote C++ program to determine the shortest path from a starting room (node) to an ending room.
-Compared travel times when stairs can be used vs. when only elevators can be used

## Challenges we ran into

-Finding the best way to give directions
-Mapping room numbers with nodes
-Errors with the algorithm

## Accomplishments that we're proud of

-We calculated the average travel time across all pairs of nodes:
-With stairs: About 100.385 seconds.
-Only elevators: About 129.818 seconds.
-Travel is ~29.3% slower for those unable to use stairs!

## What we learned

-Shortest path algorithms
-MIT's floor plans

## What's next for Dijkstra's for Disability

-Implement GUI / turn program into an app!
-Add more descriptive directions
-Use better heuristics for travel time
-Expand area of interest to entire MIT Campus (and beyond)!
## Inspiration

As freshmen new to MIT’s campus, we’ve all gotten lost our fair share of times among the confusing building names and connections between them. The maps currently available to us through the Internet and the MIT mobile app are only useful enough to tell us where each building is, but not how to get to specific rooms within those buildings, which is often why freshmen get lost in the first weeks of the semester. We wanted to build a system that would provide us with directions to get to a specific room on MIT’s campus, between floors, buildings, and even the underground tunnels.

## What it does

3D MIT gives indoor directions between rooms within MIT buildings. Ever felt lost, not been able to find a specific room for an event? 3D MIT is designed to solve your directional problems!

## How we built it

MIT publishes floorplans of all of its buildings online. We used Python and OpenCV to pull information out of these floorplans to create routes from one room to another, and then displayed this information on a webpage.

## Challenges we ran into

As one might expect, the image processing was the most challenging part of the project. We tried extracting room numbers using Google’s Tesseract OCR Engine, but we weren’t able to get it working by the end of the hackathon.

## Accomplishments that we're proud of

We’re proud of the fact that we participated, and of the effort we put in. We also learned a lot about how to use a lot of new tools and overcame many of the interesting challenges involved in creating a 3D map of MIT.

## What we learned

How to use text recognition and image manipulation (Tesseract and OpenCV)
How to use Git, JQuery, and HTML
Various shortest path algorithms and how they work (Dijkstra’s algorithm, Floyd-Warshall)

## What's next for 3DMIT

Recognize room labels using OCR
Improve website design
Use Google Maps to help direct users between MIT buildings
Options for wheelchair accessibility
Search (for the nearest bathroom, for instance)
Give directions for shortest walking distance or least time spent outside
Providing mobile device support or application for on-the-go use
Give directions based on user’s class schedules
## 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