Week #5 #

Weekly Tasks #

Feedback Collection #

To enhance our game, we focused on gathering comprehensive feedback through the following activities:

• Schedule Meetings or Interviews: We scheduled a meeting with a colleague of our game developer to collect feedback on the current version of our game. This feedback was crucial for refining our product.

• Prepare a Feedback Collection Plan: We prepared a plan that included specific open-ended questions to gather detailed feedback. This approach allowed us to capture in-depth insights without restricting respondents to predefined options.

• Conduct User Surveys and Feedback Sessions: We conducted open-ended feedback sessions to gather input on usability, functionality, and overall satisfaction. This helped us identify areas for improvement.

• Document Feedback: We meticulously documented feedback to identify specific areas for enhancement. This documentation was essential for analyzing common themes and patterns.

• Analyze Feedback: We analyzed the collected feedback to identify recurring issues and prioritize improvements based on their impact and feasibility.

Weekly Progress Report #

This week, we made significant strides in our project:

• Completed Multiplayer Mode: We successfully implemented multiplayer mode, enabling players to compete against each other in real-time.

• Completed Data Collection: We finished collecting data for training our machine learning model.

• Trained Base Model: We trained our initial machine learning model using the collected data.

• Integrated ML Model into Unity: We integrated the trained model into Unity for a separate live demo

• Built Base UI: We developed the base user interface, focusing on ease of use and intuitive design.

• Collected Feedback: We sent our MVP game to a specialized game development chat with qualified specialists and gathered valuable feedback.

Feedback Highlights #

1. Feedback on Hand Activity:

   • “The game looks promising, but I think the hand gestures might be tiring for long play sessions. Consider adding breaks or alternative controls.”

2. Positive Feedback on New User Experience:

   • “I loved the innovative use of hand gestures for spellcasting. It’s a fresh and engaging experience that stands out from traditional games.”
   • “The integration of machine learning for gesture recognition is fascinating. It adds a unique layer of interaction and fun to the gameplay.”

Challenges & Solutions #

• Challenge: Ensuring the accuracy and reliability of gesture recognition.
  • Solution: Conducted thorough research to find best practices for training and optimizing our model, ensuring high accuracy and reliability.

Conclusions & Next Steps #

This week, we successfully built the base game mechanics and multiplayer mode, completed data collection, trained our initial model, integrated it into Unity, and developed the base UI. We received valuable feedback highlighting both areas for improvement and positive aspects of our game.

Next Steps:

• Finish the graphic elements for all game features.
• Collect more diverse data to enhance the robustness of our machine learning model.
• Improve model performance by refining its accuracy and efficiency.
• Fully integrate the enhanced model into the game for a seamless experience.

By staying motivated, communicating effectively, and embracing feedback, we aim to deliver a polished and engaging game that meets our original vision.