Concept Selection

After thorough evaluation of alternatives, the following design concept was selected for implementation:

1. User Interface (UI): Minimalist Design

   • Reason for Selection:

     • Prioritizes simplicity and ease of navigation for a wide range of users.

     • Reduces cognitive load and supports scalability for future feature integration.

     • Requires fewer development resources compared to dynamic, animated designs.

2. User Workflow: Centralized Navigation

   • Reason for Selection:

     • Familiar to most users, ensuring an intuitive and consistent experience.

     • Allows quick access to main features like Home, Scanner, Create, and Collections.

     • Avoids rigid workflows, providing flexibility for advanced users.

3. Machine Learning: Inspiration Image Matching

   • Reason for Selection:

     • Directly addresses the primary user need for creating outfits based on inspiration images.

     • Feasible to implement with existing machine learning tools and datasets.

     • Provides a unique, differentiating feature compared to other wardrobe apps.

4. Virtual Closet Organization: Category-Based with Optional AI Tagging

   • Reason for Selection:

     • Offers users a familiar manual organization method while incorporating automation for those who prefer it.

     • Balances simplicity and innovation, appealing to both casual and advanced users.

     • Reduces errors and improves efficiency with optional AI-powered tagging.

5. Scanner Technology: Advanced Scanner with AI Edge Detection

   • Reason for Selection:

     • Differentiates the app by offering a high-precision, automated scanner similar to professional applications.

     • Enhances user satisfaction by reducing the time and effort required for photo editing.

     • Aligns with the project’s emphasis on cutting-edge technology.

Trade-Off Analysis

1. System Performance:

• A minimalist UI and centralized navigation improve app responsiveness by reducing computational overhead.

• The advanced scanner and AI tagging are computationally intensive but provide significant user benefits. These features will be optimized for performance to balance resource usage.

2. Design Features:

• Selected features prioritize functionality and user engagement while maintaining an approachable learning curve.

• Phased implementation of AI tagging and ML-based recommendations ensures consistent progress without overwhelming development resources.

3. Practicality:

• The chosen design leverages existing cross-platform tools (e.g., Flutter or React Native) and machine learning frameworks (e.g., TensorFlow or PyTorch), making it feasible within the project timeline and budget.

• Minimalist design and centralized navigation simplify development and testing processes.

4. Nontechnical Issues:

• Accessibility: A clean interface and logical workflow cater to users with varying technical skills.

• Scalability: The design supports future updates, including trend-based outfit suggestions and enhanced closet management tools.

• Stakeholder Feedback: The selected design reflects user preferences for simplicity, accuracy, and personalized functionality.