## Inspiration

Jessica here - I came up with the idea for BusPal out of expectation that the skill has already existed. With my Amazon Echo Dot, I was already doing everything from checking the weather to turning off and on my lights with Amazon skills and routines. The fact that she could not check when my bus to school was going to arrive was surprising at first - until I realized that Amazon and Google are one of the biggest rivalries there is between 2 tech giants. However, I realized that the combination of Alexa's genuine personality and the powerful location ability of Google Maps would fill a need that I'm sure many people have. That was when the idea for BusPal was born: to be a convenient Alexa skill that will improve my morning routine - and everyone else's.

## What it does

This skill enables Amazon Alexa users to ask Alexa when their bus to a specified location is going to arrive and to text the directions to a phone number - all hands-free.

## How we built it

Through the Amazon Alexa builder, Google API, and AWS.

## Challenges we ran into

We originally wanted to use stdlib, however with a lack of documentation for the new Alexa technology, the team made an executive decision to migrate to AWS roughly halfway into the hackathon.

## Accomplishments that we're proud of

Completing Phase 1 of the project - giving Alexa the ability to take in a destination, and deliver a bus time, route, and stop to leave for.

## What we learned

We learned how to use AWS, work with Node.js, and how to use Google APIs.

## What's next for Bus Pal

Improve the text ability of the skill, and enable calendar integration.
## Inspiration

We wanted to get home safe

## What it does

Stride pairs you with walkers just like UBC SafeWalk, but outside of campus grounds, to get you home safe!

## How we built it

React Native, Express JS, MongoDB

## Challenges we ran into

Getting environment setups working

## Accomplishments that we're proud of

Finishing the app

## What we learned

Mobile development

## What's next for Stride

Improve the app
## Inspiration

We were really excited to hear about the self-driving bus Olli using IBM's Watson. However, one of our grandfather's is rather forgetful due to his dementia, and because of this would often forget things on a bus if he went alone. Memory issues like this would prevent him, and many people like him, from taking advantage of the latest advancements in public transportation, and prevent him from freely traveling even within his own community.

To solve this, we thought that Olli and Watson could work to take pictures of luggage storage areas on the bus, and if it detected unattended items, alert passengers, so that no one would forget their stuff! This way, individuals with memory issues like our grandparents can gain mobility and be able to freely travel.

## What it does

When the bus stops, we use a light sensitive resistor on the seat to see if someone is no longer sitting there, and then use a camera to take a picture of the luggage storage area underneath the seat.
We send the picture to IBM's Watson, which checks to see if the space is empty, or if an object is there.
If Watson finds something, it identifies the type of object, and the color of the object, and vocally alerts passengers of the type of item that was left behind.

## How we built it

**Hardware**
Arduino - Senses whether there is someone sitting based on a light sensitive resistor.
Raspberry Pi - Processes whether it should take a picture, takes the picture, and sends it to our online database.

**Software**
IBM's IoT Platform - Connects our local BlueMix on Raspberry Pi to our BlueMix on the Server
IBM's Watson - to analyze the images
Node-RED - The editor we used to build our analytics and code

## Challenges we ran into

Learning IBM's Bluemix and Node-Red were challenges all members of our team faced. The software that ran in the cloud and that ran on the Raspberry Pi were both coded using these systems. It was exciting to learn these languages, even though it was often challenging.

Getting information to properly reformat between a number of different systems was challenging. From the 8-bit Arduino, to the 32-bit Raspberry Pi, to our 64-bit computers, to the ultra powerful Watson cloud, each needed a way to communicate with the rest and lots of creative reformatting was required.

## Accomplishments that we're proud of

We were able to build a useful internet of things application using IBM's APIs and Node-RED. It solves a real world problem and is applicable to many modes of public transportation.

## What we learned

Across our whole team, we learned:

* Utilizing APIs
* Node-RED
* BlueMix
* Watson Analytics
* Web Development (html/ css/ js)
* Command Line in Linux