TimeFlow: Your attention manager

Home Screen + Schulte Table Test
Message Screen
Launch Screen
Home Screen
Attention Tracker Data
Schult's Table z-score

Inspiration

Nowadays, due to rapid development of electronic devices, people are easily distracted than ever. To mitigate this problem, several apps such as Forest were developed to help people concentrate by blocking all notifications and taking full control of the phone. However, using such application is tedious since the user must input time to get it to start counting down. Moreover, people often take more breaks than the break they originally wanted to have. Therefore, we introduce an iOS application called TimeFlow to solve this issue.

What it does

TimeFlow is an iOS-based mobile application that helps people optimize their learning efficiency. Further, this application allows setting an amount of time they want to study. For the first time, the user is prompted to do a Schulte Grid attention test. After the first couple of learning cycles, every time the user sets a period of time to study, the app will automatically split the bigger task into smaller tasks. For example, if a user wants to study for 3 hours. The app may split it into six 30-minute tasks with break time inserted in between. More specifically, the 30-minute time period was learnt by the app to ensure the user has optimal learning efficiency.

How we built it

We used state of the art technologies to bring our idea to life. For the backend, we have Flask server running on the google cloud platform. For the chat, we used Firebase. Our machine learning models were trained on IBM cloud virtual machines. The main App is built using Xcode and Swift programming language.

Challenges we ran into

There were a lot of challenges that we ran into but most of them were overcomeable. The challenge that took most of our time to overcome was the problem of model training with limited availability of data. To solve this, we went ahead and created our own datasets. From finding users online to do tests on Schulte Grids and collecting their data to train our models for the most efficient outcome, we were able to solve the data problem.

Accomplishments that we're proud of

Since this was our first time working with Xcode and swift, we really think creating a functional app in such a limited time is a huge hurdle that we crossed successfully.

What we learned

Using a highly efficient method to collect data is crucial given a very short period of time. What's next for TimeFlow For future implementations of this App, we plan to improve our ML models to give users more efficient data. We also plan to enhance our group study feature where users far apart can study together with the help of this App and focus ahead.