Adaptive Augmented Reality Architecture for Optimising Assistance and Safety in Industry 4.0

• Published in: Big data and cognitive computing Vol. 9; no. 5; p. 133
• Main Authors: Morales Méndez, Ginés, del Cerro Velázquez, Francisco
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2025
• Subjects: adaptive architecture; Adaptive systems; Analysis; Architecture; assistance; Augmented Reality; Cognitive load; Completion time; Customization; Digital twins; Efficiency; Ergonomics; Industrial applications; Industrial safety; industry; Industry 4.0; Modular design; Occupational safety; Predictive maintenance; Preventive maintenance; Productivity; Real time; Safety and security measures; security; training; User experience; Virtual reality; Work environment; United States; Switzerland; Arkansas
• ISSN: 2504-2289; 2504-2289
• DOI: 10.3390/bdcc9050133

The present study proposes adaptive augmented reality (AR) architecture, specifically designed to enhance real-time operator assistance and occupational safety in industrial environments, which is representative of Industry 4.0. The proposed system addresses key challenges in AR adoption, such as the need for dynamic personalisation of instructions based on operator profiles and the mitigation of technical and cognitive barriers. Architecture integrates theoretical modelling, modular design, and real-time adaptability to match instruction complexity with user expertise and environmental conditions. A working prototype was implemented using Microsoft HoloLens 2, Unity 3D, and Vuforia and validated in a controlled industrial scenario involving predictive maintenance and assembly tasks. The experimental results demonstrated statistically significant enhancements in task completion time, error rates, perceived cognitive load, operational efficiency, and safety indicators in comparison with conventional methods. The findings underscore the system’s capacity to enhance both performance and consistency while concomitantly bolstering risk mitigation in intricate operational settings. This study proposes a scalable and modular AR framework with built-in safety and adaptability mechanisms, demonstrating practical benefits for human–machine interaction in Industry 4.0. The present study is subject to certain limitations, including validation in a simulated environment, which limits the direct extrapolation of the results to real industrial scenarios; further evaluation in various operational contexts is required to verify the overall scalability and applicability of the proposed system. It is recommended that future research studies explore the long-term ergonomics, scalability, and integration of emerging technologies in decision support within adaptive AR systems.