Mixed Reality
Laboratory Training.

Automated laboratory workflows still depend on manual preparation: placing materials, checking consumables and knowing what goes where.

At DALSA, the combination of equipment and preparation tasks can change between experiments. People unfamiliar with a particular setup need to understand both the sequence and the physical location of each task.

The project explored whether mixed reality could bring that information into the working environment, alongside specialist support. The aim was a working concept and an initial usability evaluation.

I visited the laboratory at least weekly during the approximately 12-week project, worked through existing equipment procedures and discussed needs with laboratory staff.

Input came from an intelligent systems specialist, a laboratory technician, an automation specialist and a laboratory manager. These were informal contextual discussions, supported by observations and development notes.

The intended users were people unfamiliar with a specific workflow, setup or piece of equipment — including experienced laboratory staff encountering something new.

This was contextual exploration, not a formal interview study with coded qualitative analysis.

I built the prototype in Unity for Meta Quest 3, using passthrough, world-space UI, simplified 3D equipment and target anchors.

JSON workflow content is imported into setup data. A checklist manager maintains the active step and updates both the tablet and spatial guidance. Keeping content, state and presentation separate makes the same interface reusable across equipment selections.

The prototype represents four pieces of equipment: a Hamilton Microlab STAR, a Universal Robots cobot, a Biometra T-Robot thermocycler and a PerkinElmer EnVision plate reader.

The separate formative pilot involved one participant. Instructions were easier to follow than manual setup placement, and equipment references needed to be clearer.

The pilot informed targeted clarity and stability changes before the final evaluation. The underlying workflow and alignment approach stayed within the project’s existing scope.

• More specific language. Replaced generic robot references with configured equipment names.
• More legible targets. Added subtle, low-opacity clear-area highlights.
• A cleaner tutorial. Corrected a missing SHOW ME WHERE arrow and repositioned the ending panel.
• Less visual noise. Removed obsolete helpers, a floating tutorial menu button and an unwanted completion effect.

The pilot’s SUS score was 70/100. Pilot and final scores came from different single participants; they do not demonstrate a measured usability improvement.

The biggest lesson was the gap between a technically available interaction and a learned behaviour.

That lesson shaped three priorities for future development. These are proposed improvements, not features already implemented or tested.

1. Make tablet modes explicit.

   Clarify Follow/Pin state, practise it in a realistic task and simplify long-distance repositioning. Then test whether users carry that understanding into training.

2. Start with the workflow.

   The current equipment-first setup assumes users know which machines they need. A workflow-first approach could derive the equipment, materials and preparation steps.

3. Broaden the evidence.

   Test with more people across the intended user group, repeat alignment checks under controlled conditions and verify save/load and spatial relocalization.

For me, the project brought research, visual communication and engineering into one process: understand the situation, build something tangible, and use what happens in practice to guide the next decision.