VR Glucometer Training

The context

This module was developed as part of the 75+ VR training library built for the French Ministry of Education - a national vocational programme that needed practical, hands-on skill training delivered at scale. In this multi-year initiative, the Romania team initiated the programme and nursery modules, while our Six Red Marbles India team joined to build major modules like the Hydrogen Factory and Hospital procedures. The Glucometer module was a standout within the Hospital procedures domain because it combined precise fine-motor procedure with real clinical consequences for error: wrong technique means wrong readings, and wrong readings mean wrong treatment decisions.

The challenge

Glucometer training is deceptively complex. The device itself is simple, but the procedure around it is exacting: hand hygiene, lancet handling, correct strip insertion, adequate blood-sample volume, timing, result interpretation, and proper disposal. In a classroom, instructors demonstrate once and students practise on each other - but mistakes with lancets and contaminated strips carry real risk and real cost. The training needed to be repeatable, scoreable, and safe enough to let students fail dozens of times without consequence.

The framework & technology choices

To ensure high-fidelity interactions and technical consistency across the entire 75+ module library, we selected UltimateXR as our primary VR framework, explicitly choosing it over Unity’s default XR Interaction Toolkit (XRI). This decision was based on two major advantages:

• -Rigged hand models & tracking - UltimateXR includes high-quality, fully rigged hand models with inverse kinematics (IK) out-of-the-box. This allowed us to build highly realistic hand tracking for delicate steps like lancet handling and narrow test strip insertion.
• -C# programmatic interactions - Rather than relying on heavy Unity Inspector configurations, which are difficult to version control and review, UltimateXR keeps the interaction logic clean and programmatically driven in C#. This minimized our Inspector footprint, keeping code consistent and merge conflicts to a minimum.

We built these interactions on top of a customized framework distributed to all modules as a Git submodule. This shared framework managed the starting scene, the tutorial/onboarding scene, language toggling, Moodle LMS integration, and the built-in PC Compatibility mode (enabling standard keyboard + mouse desktop fallback so learners without VR headsets could complete the same training).

The approach

Built on top of the shared Git submodule framework, this module simulates the full glucometer workflow in VR - from opening the kit to disposing of sharps. Every step is tracked and scored: pick up the right strip, insert it correctly, lance the correct finger site, apply sufficient blood, wait for the reading, interpret the result, and dispose safely. The simulation also covers the error states trainees rarely encounter in real life but must recognise instantly: expired test strips, insufficient sample volume, calibration failures, and out-of-range readings. PC Compatibility mode ensures that trainees without a headset can run the exact same procedure using standard keyboard and mouse controls.

What makes it stand out

• -Rigged hand models - Delicate movements like strip handling, lancing, and device buttons feel natural and look visually accurate.
• -Full device workflow - unboxing → hand hygiene → strip insertion → lancing → blood application → reading → sharps disposal. Nothing skipped.
• -Error-state training - expired strips, insufficient sample, calibration codes, E-codes on display. Trainees learn what to do when things go wrong, not just when they go right.
• -Objective scoring - every step is tracked. Instructors get a pass/fail breakdown per trainee, per attempt - no more subjective observation in a room of 20 students.
• -Zero consumable cost - real training burns through test strips (~€0.50 each), lancets, and control solution. VR training uses none.
• -Safe repetition - students can fail, retry, and build muscle memory without sharps-injury risk or cross-contamination concerns.

Deployment

The primary deployment target was the Meta Quest 2, with testing and verification performed on the Meta Quest 3. Every release automatically generated both the VR build and the desktop PC Compatibility build. The compilation and asset packaging were managed fully through a GitHub Actions CI/CD pipeline, turning builds into a quick, automated process.

The outcome

Trainees arrive at their first real-patient practicum having already completed the procedure dozens of times. Instructors report that students trained in VR handle the device with noticeably more confidence and make fewer procedural errors during their first live session. The module ships as part of the broader 75+ suite, built and released automatically through the same CI/CD pipeline - no special handling required.

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