Automating creation of high-fidelity holographic hand animations for surgical skills training using mixed reality headsets
Bibliographic record
Abstract
<strong>Purpose: </strong>Virtual holographic simulation skills training has a growing role in supplementing surgical skills training given increasingly limited healthcare resources and recent advancements in mixed reality technology. However, creating highfidelity 3D holographic animations (particularly hand animations) is expensive, time-consuming, and complex. We propose a low-cost solution using mixed reality headsets for motion capture of hands to automatically create high-fidelity 3D holographic hand animations. <strong>Methods:</strong> In this study, a 3D animation of a single-handed knot tie was created using the Oculus Quest 2 and APS Mocap Fusion app for display on a Microsoft HoloLens 2. To assess the feasibility and quality of the created 3D holographic animation, a qualitative and quantitative pilot study of 20 participants was conducted comparing learning one-handed knot ties from an in-person demonstration versus the 3D holographic hand-tie animation. <strong>Results:</strong> Our pilot study demonstrated participants were able to learn one-handed knot ties from the holographic animation (70% of participants) and was comparable to in-person (80%). Promisingly, based on the Likert scale questionnaire, participants found learning from the holographic animation was more effective (4.4 vs 3.3), easier (3.6 vs 3.3), and felt more confident in learning the knot-tie (4.4 vs 3.5) in comparison to in-person demonstration. Furthermore, participants felt the holographic animation was comparable to real-life hands (4). Overall, we successfully illustrated a low-cost automated methodology of creating high-fidelity 3D holographic hand animations from mixed reality headset motion capture data with potential for use in surgical simulation skills training.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".