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ASLingo: A Mobile Learning Application for American Sign Language (ASL) -…
ASLingo: A Mobile Learning Application for American Sign Language (ASL)
Sprint 1 - Planning
Define the scope of the project, including the features of camera module integration and the basic user interface
Gather initial requirements and conduct beneficiary interviews
Begin data collection for training the ASL recognition models
Sprint 2 - Prototyping and Data Preparation
Develop the prototype for the camera module integration and the basic ASL sign detection
Collect and preprocess ASL gesture data for training models
Explore and select suitable machine learning algorithms and frameworks for ASL recognition
Sprint 3 - Model Training and Integration
Train the initial version of machine learning models using the collected ASL gesture data
Evaluate the model's performance and repeatedly train the model to improve its accuracy and reliability
Integrate the trained model with the camera module and application backend for real-time ASL recognition
Sprint 4 - Feature Development and Enhancement
Develop interactive lessons for ASL learning based on the integrated ASL recognition's capabilities
Refine the user interface and user experience based on early user testing and feedback
Conduct a performance test and optimize accordingly to ensure a smooth operation of the ASL recognition in different scenarios
Sprint 5 - Testing and Refinement
Perform integration testing to ensure smooth operation of all components of the application
Conduct a user acceptance test (UAT) to validate the application's functionality and user experience
Refine the ASL recognition model and algorithm based on the feedback of the user and performance metrics of the application
Sprint 6 - Deployment and Post-Launch Optimization
Deploy the application and monitor the performance
Gather user feedback and analytics to determine which areas need further improvement
Plan future sprints or updates based on the feedback, specifically user's needs and emerging technologies