Lecture 3: Evaluating Locomotion Technique

To test the effectiveness of my “Dynamic Curve Arm Swing Flight” technique, I invited two male volunteers to participate in the test.

Participants: 3 (myself + 2 male classmates).
Test task: Complete the entire Parkour course and collect as many coins as possible.
Evaluation metrics: We recorded the completion time, coin collection accuracy, and conducted a subjective survey (1-10 scale) on motion sickness, workload, presence, and enjoyment.

Here’s the detailed performance of the three participants:

We calculated the average results for the three participants:

A. Performance
- Average Time per Round: 134.1 seconds
  - Analysis: Participant B was the fastest but missed more coins. Participant A was more careful, taking the longest time.
- Average Accuracy: 94.2%
  - Calculation: (68 + 65 + 62) / (69*3) ≈ 94.2%.
- Average Completed Rounds: 1 (all participants successfully finished the course).
B. Subjective Measures (1-10 scale)

Motion Sickness Control: The average score was only 3.0, showing that my “Dynamic Curve” algorithm helps smooth out acceleration, preventing strong nausea, even for participant A who swung their arms more.
Learning Curve: Participant B reported that although swinging arms was tiring (Workload), it felt very real, like “rowing” or “flying,” which significantly increased the sense of presence (7.7) and enjoyment (8.7).
Accuracy vs. Speed Trade-off: Data showed that when participants aimed to finish in under 130 seconds (like participant B), the coin miss rate increased. This suggests that the technique could be optimized for fine-tuning at high speeds.

Here is the core logic I used in Unity to handle controller speed and apply the dynamic curve: