## Inspiration

Mental health is of utmost importance. As it affects how we think, feel and act as we cope with life. It also helps determine how we handle stress, relate to others, and make choices. Mental health is important at every stage of life, from childhood and adolescence through adulthood and aging.

## What it does

For those uneasy in acquiring therapy, online counseling is the best to reduce their social stigma. With a online counseling, they can share stress, life experiences, problems privately with counselor comfortably, without visiting the counselor's office. We developed a website that gives its users online counselling. They can book their appointments without the hassle of going to a therapist's office.

## How we built it

The frontend is built using html, css. Express-js and Node-js frameworks are used for server side. Mongodb database was used to link all the records of the users and the counsellors.

## Challenges we ran into

Getting the backend connected to the mongodb database.

## Accomplishments that we're proud of

Getting the project up and running is one of the biggest checkpoints that we are absolutely proud of and helping the community.

## What we learned

Mental health is a very important issue and people should not shy away from it. It should be given proper care and people should be in a healthy headspace.

## What's next for Peaceful Mind

We wish to give back more to the community and making a significant change in the way we look at mental health.
## Inspiration

Studies approximate that 1 in 5 American adults suffer from a mental illness each year. In addition, nearly half of those who do suffer from mental illnesses don't seek treatment or help. As university students, friends, and family members, we've seen how mental health has the ability to impact social, academic and work lives. Therefore, we wanted to create a tool that leverages existing mental health survey data in order to promote awareness of mental health issues and encourage companies and organizations to invest in the mental well being of their employees.

## What it does

Our application is intended for employers and organizations to see how aware their policies and culture is of mental illnesses within the workplace. User would need to fill out a short survey on some characteristics of their company or organization such as the number of employees, location, mental health programs, health benefits and ease to get help. Peace of Mind uses this data and compares it historical mental health survey data in order to assign the user's organization a mental health awareness score. This score would be accompanies with research backed suggestions on what they could implement within the organization to raise the score or what they should continue to pursue in order to make sure that the workplace is a healthy environment for their employees. We wanted to show that companies have a lot to gain, economically and ethically, by having healthy, happy, and productive employees in their offices. Therefore, today employees should be able to count on their employers' support as they face mental health illnesses.

## How we built it

We got our data from a public mental health survey that 2014 and 2016. We then cleaned and prepared this data in Microsoft Excel. Then we used Microsoft Azure Machine Learning Studio in order to construct our model to predict how likely a company may be contributing to the mental health illnesses of their employees. Tuning our model involved considerations of which column of data would be important to this final prediction. For example, companies and organizations with mental health benefits and a wellness program that educated employees about mental health services would be more likely to have their employees seek help and recover quicker. We then built a web application using html/css/javascript that acted as an interface to inform visiting users about mental health statistics, calculate a mental health awareness score based on a short survey, and give a little background on the research that supports the score. The application was deployed using Microsoft Azure.

## Challenges we ran into

We ran into challenges with integrating our web service model with our web application through http requests. This was due to Microsoft Azure Machine Learning Studio needing a workaround to send http requests by using a python script or wrapping in another API. Therefore, we did not have enough time to create a full flow on the application and work on the interface for the survey entry and about portions of the web page.

## Accomplishments that we're proud of

We are proud of getting a useful model built to predict whether an employee would go seek help for a mental illness using pretty sparse mental health survey data. Our model achieved a 0.76 F1 score given less than 2000 data points with a few columns after cleaning. Given that this survey is currently being performed again in 2017, there will be even more data available in the near future to train and improve our model with.

## What we learned

This was the first machine learning project that we as individuals and as a team tackled so it was interesting to learn about the work and methods needed to clean and prepare data sets to become usable. We also learning about different machine learning algorithms and how tuning their parameters makes better models to predict our data.

## What's next for Peace of Mind

Since we have a few separate parts of our project done, the next step is to integrate all of them into a single user experience that someone would be able to use. We will also be excited to see when the mental health survey data for 2017 comes out so that there is more data to train our model. Overall, we hope that our project achieves its goal of promoting awareness for mental health and hopefully can inspire the investment in programs that can help prevent and treat mental illnesses.
## Inspiration

This project was inspired by the Professional Engineering course taken by all first year engineering students at McMaster University (1P03). The final project for the course was to design a solution to a problem of your choice that was given by St. Peter's Residence at Chedoke, a long term residence care home located in Hamilton, Ontario. One of the projects proposed by St. Peter's was to create a falling alarm to notify the nurses in the event of one of the residents having fallen.

## What it does

It notifies nurses if a resident falls or stumbles via a push notification to the nurse's phones directly, or ideally a nurse's station within the residence. It does this using an accelerometer in a shoe/slipper to detect the orientation and motion of the resident's feet, allowing us to accurately tell if the resident has encountered a fall.

## How we built it

We used a Particle Photon microcontroller alongside a MPU6050 gyro/accelerometer to be able to collect information about the movement of a residents foot and determine if the movement mimics the patterns of a typical fall. Once a typical fall has been read by the accelerometer, we used Twilio's RESTful API to transmit a text message to an emergency contact (or possibly a nurse/nurse station) so that they can assist the resident.

## Challenges we ran into

Upon developing the algorithm to determine whether a resident has fallen, we discovered that there are many cases where a resident's feet could be in a position that can be interpreted as "fallen". For example, lounge chairs would position the feet as if the resident is laying down, so we needed to account for cases like this so that our system would not send an alert to the emergency contact just because the resident wanted to relax.
To account for this, we analyzed the jerk (the rate of change of acceleration) to determine patterns in feet movement that are consistent in a fall. The two main patterns we focused on were:

1. A sudden impact, followed by the shoe changing orientation to a relatively horizontal position to a position perpendicular to the ground. (Critical alert sent to emergency contact).
2. A non-sudden change of shoe orientation to a position perpendicular to the ground, followed by a constant, sharp movement of the feet for at least 3 seconds (think of a slow fall, followed by a struggle on the ground). (Warning alert sent to emergency contact).

## Accomplishments that we're proud of

We are proud of accomplishing the development of an algorithm that consistently is able to communicate to an emergency contact about the safety of a resident. Additionally, fitting the hardware available to us into the sole of a shoe was quite difficult, and we are proud of being able to fit each component in the small area cut out of the sole.

## What we learned

We learned how to use RESTful API's, as well as how to use the Particle Photon to connect to the internet. Lastly, we learned that critical problem breakdowns are crucial in the developent process.

## What's next for VATS

Next steps would be to optimize our circuits by using the equivalent components but in a much smaller form. By doing this, we would be able to decrease the footprint (pun intended) of our design within a clients shoe. Additionally, we would explore other areas we could store our system inside of a shoe (such as the tongue).