## Inspiration

Over one-fourth of Canadians during their lifetimes will have to deal with water damage in their homes. This is an issue that causes many Canadians overwhelming stress from the sheer economical and residential implications.

As an effort to assist and solve these very core issues, we have designed a solution that will allow for future leaks to be avoidable. Our prototype system made, composed of software and hardware will ensure house leaks are a thing of the past!

## What is our planned solution?

To prevent leaks, we have designed a system of components that when functioning together, would allow the user to monitor the status of their plumbing systems.

Our system is comprised of:

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> Two types of leak detection hardware
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> * Acoustic leak detectors: monitors abnormal sounds of pipes.
> * Water detection probes: monitors the presence of water in unwanted areas.
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>
>

Our hardware components will have the ability to send data to a local network, to then be stored in the cloud.

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> Software components
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> * Secure cloud to store vital information regarding pipe leakages.
> * Future planned app/website with the ability to receive such information
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>
>

## Business Aspect of Leakio

Standalone, this solution is profitable through the aspect of selling the specific hardware to consumers. Although for insurance companies, this is a vital solution that has the potential to save millions of dollars.

It is far more economical to prevent a leak, rather than fix it when it already happens. The time of paying the average cost of $10,900 US dollars to fix water damage or a freezing claim is now avoidable!

In addition to saved funds, our planned system will be able to send information to insurance companies for specific data purposes such as which houses or areas have the most leaks, or individual risk assessment. This would allow insurance companies to more appropriately create better rates for the consumer, for the benefit of both consumer and insurance company.

### Software

Front End:
This includes our app design in Figma, which was crafted using knowledge on proper design and ratios. Specifically, we wanted to create an app design that looked simple but had all the complex features that would seem professional. This is something we are proud of, as we feel this component was successful.

Back End:
PHP, MySQL, Python

### Hardware

Electrical

* A custom PCB is designed from scratch using EAGLE
* Consists of USBC charging port, lithium battery charging circuit, ESP32, Water sensor connector, microphone connector
* The water sensor and microphone are extended from the PCB which is why they need a connector

3D-model

* Hub contains all the electronics and the sensors
* Easy to install design and places the microphone within the walls close to the pipes

## Challenges we ran into

Front-End:
There were many challenges we ran into, especially regarding some technical aspects of Figma. Although the most challenging aspect in this would’ve been the implementation of the design.

Back-End:
This is where most challenges were faced, which includes the making of the acoustic leak detector, proper sound recognition, cloud development, and data transfer.

It was the first time any of us had used MySQL, and we created it on the Google Cloud SQL platform. We also had to use both Python and PHP to retrieve and send data, two languages we are not super familiar with.

We also had no idea how to set up a neural network with PyTorch. Also finding the proper data that can be used to train was also very difficult.

## Accomplishments that we're proud of

Learning a lot of new things within a short period of time.

## What we learned

Google Cloud:
Creating a MySQL database and setting up a Deep Learning VM.

MySQL:
Using MySQL and syntaxes, learning PHP.

Machine Learning:
How to set up Pytorch.

PCB Design:
Learning how to use EAGLE to design PCBs.

Raspberry Pi:
Autorun Python scripts and splitting .wav files.

Others:
Not to leave the recording to the last hour. It is hard to cut to 3 minutes with an explanation and demo.

## What's next for Leakio

* Properly implement audio classification using PyTorch
* Possibly create a network of devices to use in a single home
* Find more economical components
* Code for ESP32 to PHP to Web Server
* Test on an ESP32
## Inspiration

In 2012 in the U.S infants and newborns made up 73% of hospitals stays and 57.9% of hospital costs. This adds up to $21,654.6 million dollars. As a group of students eager to make a change in the healthcare industry utilizing machine learning software, we thought this was the perfect project for us. Statistical data showed an increase in infant hospital visits in recent years which further solidified our mission to tackle this problem at its core.

## What it does

Our software uses a website with user authentication to collect data about an infant. This data considers factors such as temperature, time of last meal, fluid intake, etc. This data is then pushed onto a MySQL server and is fetched by a remote device using a python script. After loading the data onto a local machine, it is passed into a linear regression machine learning model which outputs the probability of the infant requiring medical attention. Analysis results from the ML model is passed back into the website where it is displayed through graphs and other means of data visualization. This created dashboard is visible to users through their accounts and to their family doctors. Family doctors can analyze the data for themselves and agree or disagree with the model result. This iterative process trains the model over time. This process looks to ease the stress on parents and insure those who seriously need medical attention are the ones receiving it. Alongside optimizing the procedure, the product also decreases hospital costs thereby lowering taxes. We also implemented a secure hash to uniquely and securely identify each user. Using a hyper-secure combination of the user's data, we gave each patient a way to receive the status of their infant's evaluation from our AI and doctor verification.

## Challenges we ran into

At first, we challenged ourselves to create an ethical hacking platform. After discussing and developing the idea, we realized it was already done. We were challenged to think of something new with the same amount of complexity. As first year students with little to no experience, we wanted to tinker with AI and push the bounds of healthcare efficiency. The algorithms didn't work, the server wouldn't connect, and the website wouldn't deploy. We persevered and through the help of mentors and peers we were able to make a fully functional product. As a team, we were able to pick up on ML concepts and data-basing at an accelerated pace. We were challenged as students, upcoming engineers, and as people. Our ability to push through and deliver results were shown over the course of this hackathon.

## Accomplishments that we're proud of

We're proud of our functional database that can be accessed from a remote device. The ML algorithm, python script, and website were all commendable achievements for us. These components on their own are fairly useless, our biggest accomplishment was interfacing all of these with one another and creating an overall user experience that delivers in performance and results. Using sha256 we securely passed each user a unique and near impossible to reverse hash to allow them to check the status of their evaluation.

## What we learned

We learnt about important concepts in neural networks using TensorFlow and the inner workings of the HTML code in a website. We also learnt how to set-up a server and configure it for remote access. We learned a lot about how cyber-security plays a crucial role in the information technology industry. This opportunity allowed us to connect on a more personal level with the users around us, being able to create a more reliable and user friendly interface.

## What's next for InfantXpert

We're looking to develop a mobile application in IOS and Android for this app. We'd like to provide this as a free service so everyone can access the application regardless of their financial status.
## Inspiration

We realized that there often applications that make life more convenient insurance. However, we have also notice that there aren't enough insurance services that are comprehensive yet convenient enough to provide people with the right amount of help in more unexpected situations, such as property damage.

## What it does

1. Manages inventory within households, these inventories can be motioned and can be claim in case of external damage (theft, fire, flood). The use of management can help owners protect their property to a greater degree and also can help insurance company keep track of the total cost of assets that they are covering.
2. AI damage analysis tool can helps predicts the state of the property after disasters or damage. Can predict the estimated amount that the property owner can receive.

## How we built it

Android Application is built using Azure Custom Vision to detect damages. This fed through into a web API which communicates to an mobile application which the home owner uses.

## Challenges we ran into

When building the application, we had to restart on our training model due to some bugs on Microsoft Servers. Also connecting the front-end and back-end was difficult as well due to the need to transfer base64 encoded images and the sheer size of them.

## Accomplishments that we're proud of

One of the accomplishments that we're proud of is how we're able to web-scrape thousands of images and use them as relevant data for our model. Another thing we are proud of is how complete the final mobile application turned out to be.

## What we learned

Many of us learned more about React Native and Javascript. Also using the services provided by Microsoft Azure was also a worthwhile experience.

## What's next for InsureHome

A more well trained damage prediction model and mode features to do with asset management.