Posted on

Team Status Report for 4/12

Team Status Report

Risk Management:

Risk: Comparison algorithm slowing down Unity feedback

Mitigation Strategy/Contingency plan: We plan to reduce the amount of computation required by having the DTW algorithm run on a larger buffer. If this does not work, we will fall back to a simpler algorithm selected from the few we are testing now.

Design Changes:

There were no design changes this week. We have continued to execute our schedule.

Verification and Validation:

Verification Testing

Pose Detection Accuracy Testing

  • Completed Tests: We’ve conducted initial verification testing of our MediaPipe implementation by comparing detected landmarks against ground truth positions marked by professional dancers in controlled environments.
  • Planned Tests: We’ll perform additional testing across varied lighting conditions and distances (1.5-3.5m) to verify consistent performance across typical home environments.
  • Analysis Method: Statistical comparison of detected vs. ground truth landmark positions, with calculation of average deviation in centimeters.

Real-Time Processing Performance

  • Completed Tests: We’ve measured frame processing rates in typical hardware configurations (mid range laptop).
  • Planned Tests: Extended duration testing (20+ minute sessions) to verify performance stability and resource utilization over time.
  • Analysis Method: Performance profiling of CPU/RAM usage during extended sessions to ensure extended system stability.

DTW Algorithm Accuracy

  • Completed Tests: Initial testing of our DTW implementation with annotated reference sequences.
  • Planned Tests: Expanded testing with deliberately introduced temporal variations to verify robustness to timing differences.
  • Analysis Method: Comparison of algorithm-identified errors against reference videos, with focus on false positive/negative rates.

Unity Visualization Latency

  • Completed Tests: End-to-end latency measurements from webcam capture to avatar movement display.
  • Planned Tests: Additional testing to verify UDP packet delivery rates.
  • Analysis Method: High-speed video capture of user movements compared with screen recordings of avatar responses, analyzed frame-by-frame.

Validation Testing

Setup and Usability Testing

  • Planned Tests: Expanded testing with 30 additional participants representing our target demographic.
  • Analysis Method: Observation and timing of first-time setup process, followed by survey assessment of perceived difficulty.

Feedback Comprehension Validation

  • Planned Tests: Structured interviews with users after receiving system feedback, assessing their understanding of recommended improvements.
  • Analysis Method: Scoring of users’ ability to correctly identify and implement suggested corrections, with target of 90% comprehension rate.
Posted on

Rex’s Status Report for 4/12

This week, I began by implementing more key features and refactoring critical components, as a part of the integration phase of our project. I modified our pose receiving to properly handle CombinedData, which now includes both raw poseData and real-time feedback from the dynamic time warping (DTW) algorithm. This integration required careful coordination with the updated pose_sender.py script, where I also addressed performance issues with regards to a laggy webcam input. Specifically, I optimized the DTW algorithm by offloading computations to a separate thread, reducing webcam lag and improving responsiveness. Additionally, I implemented a new character skin feature compatible with Danny’s pose_sender, allowing for a more customized and engaging user experience.

Progress is mostly on schedule for the integration part. I plan to spend additional hours refining the feedback visualization and testing latency under different system loads. In the coming week, my goal is to complete the UX feature that highlights which body parts are incorrectly matched in real-time during a dance session. This will significantly enhance usability and user learning by making corrections more intuitive and immediate for the final demo as well.

Now that core modules are functioning, I’ve begun transitioning into the verification and validation phase. Planned tests include unit testing each communication component (pose sender and receivers), integration testing across the DTW thread optimization, and utilizing several short dances for testing accuracy of the real-time feedback. To verify design effectiveness, I will analyze frame-by-frame comparisons of live poses against reference poses as well as the DTW algorithm’s window. This would allow me to check timing accuracy, body part correlation, and response latency using python timers in the code; seeing that they adhere to what we outlined in the use-case requirements with regards to timing metrics. I also plan to evaluate user interaction with the feedback system via usability testing in order to see how viable the final demo can be.