## How the internet of things can build more healthy, livable, and walkable cities

**The dream of walkable cities and livable downtown areas.**

* Desire by cities to transform themselves into modern high density living environments.
* Long commutes, concerns about the environment, and changing cultural attitudes are spurring an interesting downtown living.
* Design of cities is changing to accommodate these cultural shifts. However, cities lack the data to inform the design.
* Lots of potential information which could better inform how the city is planned is currently being lost.

**The technology behind the system**

* Arduino Board
* Particle Electron Board
* Light and Photoresistor
* Humidity and Temperature Sensor
* Sound Sensor
* Gas Sensor
* Various data mining and file management software.

**Use case 1: improving walking traffic flow**

* Tracking the movement of walkers could provide many useful insights into traffic patterns.
* How do varying levels of noise impact walking? How about rain? Temperature?
* People avoid a certain dark area at night, it may seem sketchy and unsafe.
* This tracking system is anonymous. It doesn’t record who it is passing by.

**Use case 2: measuring noise and air pollution**

* Noise pollution can reduce the value of properties, can harm hearing, and can discourage walking. However, little noise pollution data is collected.
* Air pollution and bad smells can negatively impact the health of walkers. Carbon monoxide and methane are especially problematic and common compounds which would be hazardous.

**Use Case 3: Detailed city environment maps**

* The gas sensor detects several gases dangerous to human health such as methane and carbon monoxide.
* Could be correlated with everything from littering data to levels of heat/cold, which matters for environmental planning.
* Could be used for bridge lifespan analysis, giving the city detailed information about bridge usage and environmental conditions

**Conclusion**

While eager to take advantage of the new technology which the internet of things offers, cities have yet to embrace the technology on a massive scale. SmartSteps could introduce cities to the potential of sensor technology while providing them with the information they need to effectively transform their downtown areas into the highly livable and walkable areas.

Registered URL is smartsteps.tech
## Inspiration

We created ASL Bridgify to address the need for an interactive real time pose-estimation based learning model for ASL. In a post-pandemic world, we foresee that working from home and more remote experiences signals the need to communicate with individuals with hearing disabilities. It is a feature that is missing from various video conferencing, learning, and entertainment - based platforms. Shockingly, Duolingo the number 1 language learning platform does not teach ASL.

## What it does

ASLBridgify is an educational platform that specifically focuses on the learning of ASL. We provide comprehensive modules that help you learn languages in scientifically proven ways. We provide easy to follow UI and personalized AI assistance in your learning journey. We realize that the future of AI comes in more than chatbot form, so our AI models are integrated within video to track hand-movement using Media pipe and TensorFlow.

## How we built it

We created an educational platform by leveraging many technologies. Our frontend uses Next.js, Tailwind and Supabase. Our backend used Python libraries such as PyTorch, TensorFlow, and Keras to train our LLMs with the use of Intel Developer Cloud GPU and CPU to expedite the training. We connected the Frontend with the Backend with Flask. Moreover, we combined our trained models with Google Search API OpenAI API for Retrival-Augemented-Generation (RAG)

## Challenges we ran into

The biggest challenge was time. The time it took to train one Large Language Model, even when using Intel Developer Cloud GPU capabilities was immense. It was a roadblock because we couldn't test any other code on one computer until the LLM was done training. Initially we tried to preprocess both words and sentences using hand pose to map ASL and using encoder decoder architecture, but we were not able to complete this because of the time constraint. ASL sentences is something we want to incorporate in the future.

## Accomphishments we are proud of

We successfully trained preliminary large language models using PyTorch GPUs on the Intel Cloud from scratch. We're thrilled to integrate this accomplishment into our frontend. Through implementing three AI tools—each using different methods such as calling an API and building with IPEX—we've gained valuable insights into AI. Our excitement grows as we introduce our one-of-a-kind educational platform for ASL to the world.
## Inspiration

We noticed one of the tracks involved creating a better environment for cities through the use of technology, also known as making our cities 'smarter.' We observed in places like Boston & Cambridge, there are many intersections with unsafe areas for pedestrians and drivers. **Furthermore, 50% of all accidents occur at Intersections, according to the Federal Highway Administration**. This can prove to be enhanced with careless drivers, lack of stop signs, confusing intersections, and more.

## What it does

This project uses a Raspberry Pi to predict potential dangerous driving situations. If we deduce that a potential collision can occur, our prototype will start creating a 'beeping' sound loud enough to gain the attention of those surrounding the scene. Ideally, our prototype will be attached onto traffic poles, similar to most traffic cameras.

## How we built it

We utilized a popular Computer Vision library known as OpenCV, in order to visualize our problem in Python. A demo of our prototype is shown in the GitHub repository, with a beeping sound occurring when the program finds a potential collision.

Our demonstration is built using Raspberry Pi & a Logitech Camera. Using Artificial Intelligence, we capture the current positions of cars, and calculate their direction and velocity. Using this information, we predicted potential close calls and accidents. In such a case, we make a beeping sound simulating a alarm to notify drivers and surrounding participants.

## Challenges we ran into

One challenge we ran into was detecting the car positions based on the frames in a reliable fashion.

A second challenge was calculating the speed and direction of vehicles based on the present frame & the previous frames.

A third challenge included being able to determine if two lines are crossing based on their respective starting and ending coordinates. Solving this proved vital in order to make sure we alerted those in the vicinity in a quick and proper manner.

## Accomplishments that we're proud of

We are proud that we were able to adapt this project to multiple levels. Even putting the camera up to a screen of a real collision video off Youtube resulted in the prototype alerting us of a potential crash **before the accident occurred**. We're also proud of the fact that we were able to abstract the hardware and make the layout of the final prototype aesthetically pleasing.

## What we learned

We learned about the potential of smart intersections, and the benefits it can provide in terms of safety to an ever advancing society. Surely, our implementation will be able to reduce the 50% of collisions that occur at intersections by making those around the area more aware of potential dangerous collisions. We also learned a lot about working with openCV and Camera Vision. This was definitely a unique experience, and we were even able to walk around the surrounding Harvard campus, trying to get good footage to test our model on.

## What's next for Traffic Eye

We think we could make a better prediction model, as well as creating a weather resilient model to account for varying types of weather throughout the year. We think a prototype like this can be scaled and placed on actual roads given enough R&D is done. This definitely can help our cities advance with rising capabilities in Artificial Intelligence & Computer Vision!