Design and evaluation of an augmented reality simulator using leap motion
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 virtual and augmented reality (AR) are having an impact on the medical field in areas such as surgical simulation. Improvements to surgical simulation will provide students and residents with additional training and evaluation methods. This is particularly important for procedures such as the endoscopic third ventriculostomy (ETV), which residents perform regularly. Simulators such as NeuroTouch, have been designed to aid in training associated with this procedure. The authors have designed an affordable and easily accessible ETV simulator, and compare it with the existing NeuroTouch for its usability and training effectiveness. This simulator was developed using Unity, Vuforia and the leap motion (LM) for an AR environment. The participants, 16 novices and two expert neurosurgeons, were asked to complete 40 targeting tasks. Participants used the NeuroTouch tool or a virtual hand controlled by the LM to select the position and orientation for these tasks. The length of time to complete each task was recorded and the trajectory log files were used to calculate performance. The resulting data from the novices' and experts' speed and accuracy are compared, and they discuss the objective performance of training in terms of the speed and accuracy of targeting accuracy for each system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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