# Mind, Machine, and Meaning: Cognitive Ergonomics and Adaptive Interfaces in the Age of Industry 5.0
## 3 Ergonomic Techniques in Human-Machine Interactions
### Modern Solutions for Improving Human-Machine Interactions
#### 3.3.3 Augmented and Virtual Reality in Operator Training: Applications, Methods, and Examples
* Augmented Task Guidance: AR can deliver contextual assistance directly within the operator's field of vision. In industrial maintenance, AR headsets provide overlays with torque values, replacement instructions, or real-time sensor readings. For example, a worker replacing a valve may see animated instructions guiding hand motion, reducing the training time. Studies show that AR reduces the cognitive load by offloading memorization and minimizing task-switching [139]. * Mixed Reality Collaboration: This supports collaborative, spatially-aware training. Trainees can work in synchronized environments, manipulating virtual objects anchored in physical space. In a shipbuilding case, multi-user AR setups reduced communication latency and improved spatial awareness during structural assembly [139]. CAVE systems and MR telepresence also allow remote trainers to provide real-time corrections. * Deep Learning with 3D Models AR: Guidance systems can be enhanced using convolutional neural networks (CNNs) trained to recognize parts from CAD models. For instance, small parts like bolts or washers, which are often difficult to track visually, can be identified and labeled in real time. The automated generation of 2D datasets from 3D models streamlines the CNN training process, reducing the manual annotation effort [146]. * Industrial Assembly and Maintenance: AR/VR tools are widely used in manufacturing for assembly training and predictive maintenance. In the aerospace industry, Boeing uses AR to assist technicians in wiring aircraft, reducing error rates by 90%. In electronics, operators can rehearse fine-motor tasks repeatedly before touching physical parts, reducing scrap rates and downtime [141; 142]. * Chemical Industry Accident Simulation: Vidal-Balea developed an Operator Training Simulator (OTS) for chemical accidents using a combination of VR and AR. The simulation connected operators in the field and control room through a Distributed Control System (DCS). Operators reported a 4.5\(\times\) improvement in procedural familiarity and reaction time compared to traditional tabletop exercises [139]. * Shipbuilding Collaborative: AR Navantia and Universidade da Coruna deployed Microsoft HoloLens (Microsoft, Redmond, WA, USA) in shipyards to facilitate multi-user AR training. Participants experienced synchronized visualizations of complex assemblies, significantly reducing spatial misalignment and communication delays in team operations [139]. * Engineering and University Education: The University of Zilina implemented AR/VR in their Digital Factory curriculum. Students trained with virtual replicas of machinery using Unity 3D and Autodesk Maya models. Time studies (chronometry) and error tracking revealed substantial improvements in performance, demonstrating AR/VR's value for technical education [144]. * Object Recognition for Assembly: CNNs trained on synthetic image datasets allowed AR systems to recognize components and suggest actions during assembly. Thismethod is ideal for small-batch production, where frequent part changes make traditional automation costly [146]. * Construction Safety and Risk Training: [PERSON] et al. highlighted AR/VR's potential in improving safety awareness in construction. Simulations recreated fall risks, structural collapse, and equipment failure. While immersive tech increased engagement, implementation was limited by high costs, a lack of training, and initial resistance from the workforce [141]. * XR in Collaborative Education: [PERSON] and [PERSON] introduced an XR platform for engineering education. Students collaborated in real-time across locations to perform virtual repairs and maintenance, enhancing teamwork, spatial understanding, and technical skill retention [142]. Evaluation and Effectiveness  
Assessment techniques for AR/VR training include pre- and post-training knowledge tests; NASA-TLX for the cognitive workload; metrics such as the task execution time, success percentage, and total number of errors; and eye-tracking and physiological sensors for attention and stress (Table 10).