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<p>Enhancing technical training through adaptive augmented reality (AR) is crucial for improving skill acquisition and operational efficiency in industrial environments. This paper presents SAARTool, an innovative Smart and Adaptive Augmented Reality Tool designed for workforce training in olive oil sector. By integrating geometric constraints and real-time feedback, SAARTool ensures precise assembly execution and disassembly tasks, addressing key limitations in XR training solutions. SAARTool employs real-time feedback and three novel dynamic adaptive algorithms, optimizing task complexity based on user performance across 15 structured training stages guided by defined control parameters. Experimental results show that SAARTool significantly enhances practical skill acquisition, reducing execution times and improving performance consistency compared to conventional AR-based training. Statistical analyses, including Student’s T-test, Mann–Whitney U and Two-Way Anova, confirm superior SAARTool effectiveness in standardizing procedural training outcomes, particularly for individuals with limited experience. Unlike conventional AR solutions that require high-performance computing, SAARTool operates efficiently with minimal hardware, lowering adoption barriers.</p>

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Smart adaptive augmented reality SAARTool for personalized training in advanced industrial assembly operations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月12日

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  • Adonis Jose Pabuena-Garcia,
  • Jorge Manuel Mercado-Colmenero,
  • Diego Francisco Garcia-Molina,
  • Juan de Dios Caballero-Garcia,
  • Cristina Martin-Doñate

摘要

Enhancing technical training through adaptive augmented reality (AR) is crucial for improving skill acquisition and operational efficiency in industrial environments. This paper presents SAARTool, an innovative Smart and Adaptive Augmented Reality Tool designed for workforce training in olive oil sector. By integrating geometric constraints and real-time feedback, SAARTool ensures precise assembly execution and disassembly tasks, addressing key limitations in XR training solutions. SAARTool employs real-time feedback and three novel dynamic adaptive algorithms, optimizing task complexity based on user performance across 15 structured training stages guided by defined control parameters. Experimental results show that SAARTool significantly enhances practical skill acquisition, reducing execution times and improving performance consistency compared to conventional AR-based training. Statistical analyses, including Student’s T-test, Mann–Whitney U and Two-Way Anova, confirm superior SAARTool effectiveness in standardizing procedural training outcomes, particularly for individuals with limited experience. Unlike conventional AR solutions that require high-performance computing, SAARTool operates efficiently with minimal hardware, lowering adoption barriers.