Leap Motion Performance in an Augmented Reality Workspace: Integrating Devices with an Interactive Platform
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
Advances in mobile technology have enabled virtual reality (VR) and augmented reality (AR) systems to become more accessible and affordable. There are several devices that can be integrated with the mobile platform to make the applications more interactive, such as Leap Motion (LM). In this article, an AR environment has been designed that uses an Android smartphone with the LM. It has been evaluated for usability and accuracy by designing 15 sphere-targeting tasks that require the participants to use the LM to place the tip of a virtual index finger within the sphere. The task completion time and fingertip location were recorded, and the accuracy of the task was evaluated by calculating the distance between the fingertip location and the center of the sphere in three dimensions and each individual direction. Participants were the most accurate in the width and height directions, but there was a significant decrease in accuracy in the depth direction. Several participants experienced a decrease in task completion time as they progressed through the tasks, but half of the participants experienced tracking problems that increased their task completion times. Overall, the participants reported that the system was very intuitive and performed as designed; however, further improvements are needed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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