Smart Focus

• 

  Smart Focus

Inspiration:

The inspiration for Smart Focus came from a simple observation: navigating financial websites can be overwhelming for anyone, especially for individuals with ADHD. We realized that traditional web designs don’t account for the unique challenges faced by people with ADHD, such as difficulty maintaining focus and cognitive overload. This sparked our mission to create a solution that would transform chaotic digital experiences into calm, controlled environments, allowing every user to engage meaningfully with content.

What it does:

Smart Focus is an eye-tracking solution designed to enhance website accessibility for users with ADHD. By monitoring real-time attention patterns, our technology automatically adjusts the website interface to match each user’s focus needs. When attention drifts, Smart Focus gently guides the user back to important information, creating personalized "distraction-free zones" around critical content. This helps reduce cognitive overload and makes complex tasks—like managing investments—more manageable and less stressful.

How we built it:

We started by conceptualizing how we could leverage eye-tracking technology to address the specific needs of ADHD users. After researching various APIs, we implemented an existing eye-tracking API and customized it to suit our needs. We used a dot-training method, where users follow a sequence of dots on the screen to calibrate the system and accurately track their gaze. Once calibrated, our software dynamically adjusts the website layout in real-time based on where the user is looking, ensuring that distractions are minimized and focus is maintained on key areas.

Challenges we ran into:

1. Accuracy of Eye-Tracking: We have implemented 16 manual calibration points. Whenever the user starts a new session on Chrome, his first session will show 16 calibration points, which the user has to click on using this cursor. The eye-tracking will track user eyes along with the cursor, and it will train its regression model. So, there is an element of machine earning involved as well. Later, the model will keep training itself automatically; the more the user engages with the product, the higher the accuracy becomes. Additionally, there is a manual navigation feature that the user can use in case they are facing trouble with the automation feature. In this feature, they can use the up and down arrow keys to navigate through the focused content.

2. Balancing Privacy: We have downloaded the entire repository from the web gaze. Thus we are not making any CDN call to the API by using it the src element in our HTML code. Thus the data of eye tracking of the user is being used on the local system. The data is being stored in an IndexDB type database called localforage. This ensures security and privacy for the user. The eye tracking data is not going anywhere outside the local system of the user. The user just has to download this extension and plug it in their Chrome browser. This holds across all websites that the user is going to browse on Chrome. Thus, the privacy of data will always be maintained.

Accomplishments that we're proud of:

1. Creating an Inclusive Environment: Our solution has made websites more accessible for individuals with ADHD by reducing distractions and simplifying navigation.

2. Increased Client Retention: By improving user engagement through personalization, we’ve helped businesses like Capital Group retain more clients who might otherwise struggle with traditional website designs.

3. Scalability: Our system is scalable and can be extended across different platforms, making it adaptable for various industries beyond financial services.

4. Accessibility: Since this software is available as a free Chrome extension, anyone can benefit by using it. Most of the software that helps in accessibility out there is paid (thereby making it restrictive to use).

What we learned:

1. Master Gaze Coordinate Detection
2. Leverage Existing APIs for Enhanced Performance
3. Create dynamic content adjustment based on attention patterns
4. Develop a 9-point calibration system for precise gaze tracking

What's next for Smart Focus:

1. Broader Accessibility Features: Beyond ADHD, we want to explore how our technology can be adapted for other cognitive or sensory impairments, making digital experiences more inclusive for all users.

2. Advanced Machine Learning Models: We’re working on improving our machine learning algorithms so that they can better predict attention patterns and make even more accurate adjustments.

3. Mobile Integration: We aim to bring our eye-tracking solution to mobile platforms, ensuring users can benefit from distraction-free experiences across all devices.

Built With

• css
• html
• javascript
• python
• webgrazer