The problem Project Stephen solves

Project Stephen revolutionizes communication for individuals with severe physical disabilities by harnessing eye movements as a means of expression. It tackles several key issues:

1. Empowering Communication: For those unable to speak or type conventionally, Project Stephen offers a vital channel for self-expression and connection with others.

2. Independence and Dignity: By enabling direct communication, it restores agency and dignity to users, reducing reliance on others to convey their needs and desires.

3. Improved Quality of Life: Accessible communication enhances social interactions, access to services, and overall wellbeing by facilitating engagement and expression.

4. Accessibility and Inclusion: It fosters a more inclusive society by providing an alternative communication method that accommodates diverse abilities.

5. Health and Wellbeing: Effective communication aids in accessing healthcare and expressing needs, promoting better physical and mental health outcomes.

6. Cognitive Stimulation: Project Stephen stimulates the mind and combats loneliness by enabling cognitive engagement and social interaction.

7. Educational and Vocational Opportunities: It opens doors to education and employment by facilitating participation in learning and work environments.

8. Technological Innovation: Through cutting-edge technology, Project Stephen pushes the boundaries of assistive technology, inspiring further innovation in the field.

In essence, Project Stephen is a game-changer, offering a lifeline to individuals with severe physical limitations, enriching their lives, and fostering greater inclusion and participation in society.

Challenges we ran into

During the development of Project Stephen, we encountered notable challenges that required us to adapt our approach and problem-solving skills. Two main hurdles stood out: the decision to utilize VanillaJS instead of React/Next-based systems and resolving peer dependencies with TensorFlow.js.

1. Choosing VanillaJS Over React/Next-based Systems:

   Without the structure provided by React or Next.js, we had to develop custom solutions for tasks like managing state, implementing routing, and organizing the codebase. This required extra time and effort, as we needed to design and implement these functionalities from scratch. Furthermore, the absence of JSX in VanillaJS made templating and rendering more cumbersome, potentially leading to less efficient code organization and increased complexity in maintenance.

   Nonetheless, opting for VanillaJS afforded us greater flexibility and control over the project's architecture and performance. By leveraging core JavaScript features and implementing tailored solutions, we created a lightweight and efficient application that met Project Stephen's requirements.

2. Peer Dependency Resolution with TensorFlow.js:
   Integrating TensorFlow.js, crucial for implementing eye-tracking technology, presented its own set of challenges, particularly with resolving peer dependencies. TensorFlow.js relies on various dependencies like WebGL and WebAssembly for optimal performance and real-time processing of eye movements. However, addressing compatibility issues and version conflicts proved to be complex and time-consuming.

Despite these challenges, we tackled peer dependency resolution with determination and collaboration. Working closely with the development team, we overcame compatibility issues and achieved optimal performance in Project Stephen.