Virtual Reality Simulation Technology for Cardiopulmonary Resuscitation Training: An Innovative Hybrid System With Haptic Feedback
Bibliographic record
Abstract
Objective. Although cardiopulmonary resuscitation (CPR) skills are lifesaving skills, the gap between awareness and actual training remains significant. Advances in technology are shaping the future of education and innovative learning solutions are essential to facilitate effective and accessible training. This project objective is to develop a self-directed educational system for hands-on CPR training using virtual reality (VR) technology. Methods. HTC VIVE was the chosen VR engine, and Unity3D was the software used for development. CPR skills including chest compressions, rescue breathing, and automated external defibrillator (AED) are taught in VR through focused instructions, demonstrations, and simulated interactive scenarios with hands-on training sessions. A tracking system was designed using virtual planes and VIVE-Tracker for accurate measurements of chest compressions (rate, depth, and recoil), hands’ position and AED. A real mannequin was integrated in the VR space and overlaid with virtual 3D-human model for realistic haptic feedback and hands-on training. VIVE-controller was used for precise calibration between the mannequin location in real environment and the virtual human model in VR space. Results. The VR-CPR prototype was designed to be generic, approachable, and easy to follow. Realism and interaction were achieved through 3D virtual scenes simulating common sites at which cardiac arrest may occur. Variety in scenarios and gamification features like scoring and difficulty levels of training were made to enhance users’ engagement. The VR-mannequin hybrid system enabled quality training and immersive learning experience. Further, real-time feedback and scoring system are built for self-directed learning and optimal performance. Conclusions. The developed VR-hybrid product is a structured educational tool for hands-on CPR training and ongoing practice. This innovative technology provides self-directed learning with no restrictions of time, place, or personnel, which are the main challenges with current traditional courses. This product is a promising CPR training initiative in the evolution of digital education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".