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Traditional training methods in industrial environments often lack real-time guidance and interactive feedback, which can make knowledge sharing challenging. Augmented Situated Visualization (SV) can improve industrial training by providing in-depth, spatially relevant instructions, which are particularly valuable when safety procedures are crucial. This work describes a tool for SV deployed on a commercial headset and investigates how two different SV patterns, 2D labels and 3D ghosts, impact user experience, workload, discomfort, task completion time, and memory recall in a training scenario for machine maintenance.

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Enhancing Manufacturing Training Through Augmented Situated Visualization

  • 会议论文

  • 发表日期：

    2026年

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  • Zeinab BagheriFard,
  • Renan Guarese,
  • Luis Quintero,
  • Fabian Johnson,
  • Benjamin Edvinsson,
  • Mario Romero

摘要

Traditional training methods in industrial environments often lack real-time guidance and interactive feedback, which can make knowledge sharing challenging. Augmented Situated Visualization (SV) can improve industrial training by providing in-depth, spatially relevant instructions, which are particularly valuable when safety procedures are crucial. This work describes a tool for SV deployed on a commercial headset and investigates how two different SV patterns, 2D labels and 3D ghosts, impact user experience, workload, discomfort, task completion time, and memory recall in a training scenario for machine maintenance.