Mobile Gyro Control for Intuitive Manipulation of Virtual Anatomy Specimens
Bibliographic record
Abstract
The VanVR APP (Vancouver Virtual Reality Anatomy and Pathology Project) is an anatomy educational tool developed as a response to the Covid‐19 pandemic. The web app provided 3D models of scanned prosections to accommodate students with their virtual anatomy lab courses at UBC. The web interface means that interactions with the 3D specimens are controlled by mouse movements, which can be less intuitive and confusing. We have developed a technology that allows students to manipulate and turn the 3D specimens with 6 degrees of freedom using their mobile phones as a controller. The VanVR APP was created using multiple development tools such as Unity Engine for the overall environment setup and UI implementation, C# scripts for the interactive functionalities, 3D photogrammetry for specimen visualization, MongoDB for database, render Unity built WebGL and deployed the web app on Azure app service. To improve the interactivity and increase the intuitive design of the application, a new functionality of mobile gyro control was implemented using Photon to connect with the 3D specimens in WebGL using generated pairing code to match the web app and the mobile device in a virtual room. The orientation of the phone is transmitted to a spherical model in WebGL, which accordingly transforms the coordinates of any chosen specimen displayed in front of the user. This means that simply by tilting a phone like a gyro controller, users will be able to intuitively mirror how the specimen rotates in the virtual lab with significantly more freedom than the traditional mouse movement control. Along with the functionality of zooming in/out and comparing different models side by side, the app simulates the feeling of handling a specimen physically. In addition, learners can view the documented annotations tailored to each specimen illustrating details of various regions on the model. The addition of the mobile gyro control gives a more natural way to examine the specimen. This new feature highlights our student‐centred approach with an emphasis on intuitive and accessible design approaches. The acceleration in the use of virtual technologies during the pandemic has increased the use of related tools and their acceptance in anatomy education. The further development of technologies to improve the student learning experience in AR/VR will certainly broaden accessibility. Some future implementation includes haptic touch feedback on mobile phones which better simulates physical labs, and a mobile AR function that extends the current gyro controller feature, deploying AR rendered specimens assigned from the WebGL end on multiple mobile devices, which can be utilized in a classroom setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".