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pResponder Logo
Inspiration
At Artificial Inventors, our main goal coming into this hackathon was to create a project that would leave a great impact on its users. Many of us have experienced situations where a person might have an seizure, stroke, or other potentially fatal medical condition, and we were unable to assist those in need until the paramedics arrive. This led to us creating pResponder.
What it does
pResponder's primary function is to notify and enable bystanders to aid someone that needs medical attention. The user may input important medical details about themselves that may allow paramedics or bystanders to better gauge the patient's problem and provide assistance immediately.
How we built it
pResponder is based on 3 main components, an Android application that interacts with the user, the use of fitbit to collect data, and the implementation of a cloud based storage using Microsoft Azure. The Android app was built on Android Studio using Java.
Challenges we ran into
There were many challenges that we faced throughout the development of this project, however the most difficult is the problem of transferring the fitbit data to the cloud and finally to the android application.
Accomplishments that we're proud of
This hackathon has been a big learning curve for us as developers, and there are many accomplishments that we're proud of. However the greatest achievement was the successful integration of Microsoft Azure into our project.
What we learned
During the time at UofT Hacks VI, our team used many platforms that weren't familiar to us. Even though this posed as a challenge, mentors guided us along the way, and we were able to learn many new ideas, languages, and software. Furthermore, this was the first time our group implemented clouds and databases within our project, and as first year's, this opportunity provided us as a chance to learn from the many seniors and mentors here.
What's next for pResponder
We have many features that we wanted to implement, including AI. We would like to develop our program further so that it can achieve the ability to recognize symptoms based on sets of training data to maximize the chance of recognizing situations, especially for individuals who have many different conditions, allowing our application to help a wider range of people in a more efficient manner. Despite doing so much in such little time, our ideas were too complicated to successfully make everything work, and several features such as facial recognition security and a way to send your reports directly to a family doctor had to be omitted from this version.
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