Usability Comparison between 2D and 3D Control Methods for the Operation of Hovering Objects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper experimentally analyzed the cognitive load of users based on different methods of operating hovering objects, such as drones. The traditional gamepad-type control method (2D) was compared with a control method that mapped the movement directions of the drone to the natural manipulation gestures of the user using a Leap Motion device (3D). Twenty participants operated the drone on an obstacle course using the two control methods. The drone’s trajectory was measured using motion-capture equipment with a reflective marker. The distance traveled by the drone, operation time, and trajectory smoothness were calculated and compared between the two control methods. The results showed that when the drone’s movements were mapped to the user’s natural directional gestures, the drone’s 3D movements were perceived as more natural and smoother. A more intuitive drone control method can reduce cognitive load and minimize operational errors, making it more user friendly and efficient. However, due to the users’ lack of familiarity with Leap Motion, it resulted in longer distance and time and lower subjective satisfaction; therefore, a more improved 3D control method over Leap Motion is needed to address the limitations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it