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our entire team locked in after using lockedin
Inspiration
Are you always chasing assignment deadlines? Are you wishing you had more time in your day? Are you wondering why midsem break is breaking you instead of giving you a break? Good news, you are not alone, many of us have suffered and continue to suffer from long hours spent dreading through deadlines and study, and it seems to be a profound problem innate to the nature of our study habits and strategies. We continue to work hard and long hours only to find that our productivity didn't always match the amount of time invested cramming (instead of enjoying life outside of work and study). This led our team to rethink: to combat this, we knew we had to maximise productivity. With limited hours and proneness to burnouts, we've gotta make the most of our time so that we can maintain a healthy work-life balance amidst our academic pressures.
What it does
Introducing lockedin, study tracker revolutionised. Instead of competing with your friends for the next "longest" study session, compete with your friends for the next "most productive" study session. With a built-in real-time facial recognition system, the algorithm checks and keeps track of the percentage of time you spend focused. Assisted by a traditional timer, you can now keep an eye on your working efficiency and productivity alongside how long you've studied.
How we built it
We built the project using a combination of JavaScript, HTML, and CSS for the front-end, and the face-api.js library for the face detection functionality. The front-end was designed to create a user-friendly interface for tracking study productivity. We used HTML to structure the web page, CSS to style it, and JavaScript to add interactivity.
For the face detection feature, we leveraged the face-api.js library, which provides pre-trained models for face detection, recognition, and landmark detection. We loaded these models using the loadFromUri method and used them to detect faces in a live video stream from the user's webcam.
Challenges we ran into
It was challenging to integrate the real-time facial recognition alongside the other interactive features of the app. Considering this being the first hackathon participation for our group, we were somewhat thrown into the deep end with a bulk of new terminology, project concepts, and of course the technical aspects of coding and binding components in different languages.
Furthermore, since it was the first time for many of our group members to work on a group coding project collaboratively and concurrently, and we had to overcome the initial learning curve and adapt to the systematic collbaboration with github. It was also difficult to delegate initial tasks since we were not sure how to effectively split the workload into blocks.
Accomplishments that we're proud of
We are happy about the fact that the facial recognition library was implemented successfully and the merit/demerit points can be calculated with minimal lag in real-time. We were also able to achieve a large component of the project concept via the frontend.
What we learned
This gave us an amazing hands on learning opportunity with different languages, as well as working together in a team and putting together a project.
What's next for lockedin
There are further concepts that we would like implement, such as a reactive gradient / background to indicate to the user that their efficiency is dropping. And of course, integrate a more complex algorithm to calculate the efficiency percentage, via WPM tracking, eye-gaze tracking etc. Additional features for such as user accounts and leaderboards to increase user interaction.
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