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<p>Augmented reality environments face significant challenges in achieving context and semantic awareness, which are relevant for enhancing user experiences. Although machine learning has been extensively applied to areas such as object and action recognition, there is a knowledge gap in the integration of these techniques to improve contextual understanding in AR environments. This study explores how machine learning enhances object and action recognition, improving context awareness in augmented reality environments. A systematic review of fifteen studies revealed advancements in deep learning, semantic web, knowledge graphs, and multimodal interfaces, which collectively improved the capabilities of augmented reality systems. The findings include innovations in aspects such as 3D hand pose estimation, semantic gaze analysis through eye tracking, and hybrid approaches that optimize real-time processing. These developments improve augmented and mixed reality environments by enabling more realistic, interactive, and responsive applications. However, persistent challenges such as latency, scalability, and environmental adaptation underscore the need for more research. These results suggest a guide for the development of highly immersive and efficient AR/MR systems, with the potential to improve user interaction and operational capabilities.</p>

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Enhancing context awareness in augmented reality environments: a review of opportunities and challenges in object and action recognition through machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月8日

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  • Iolanda L. Chamusca,
  • Tiago P. Pagano,
  • Rafael B. Loureiro,
  • Erick G. S. Nascimento,
  • Alex Á. B. Santos,
  • Yiyu Cai,
  • Thiago B. Murari,
  • Ingrid Winkler

摘要

Augmented reality environments face significant challenges in achieving context and semantic awareness, which are relevant for enhancing user experiences. Although machine learning has been extensively applied to areas such as object and action recognition, there is a knowledge gap in the integration of these techniques to improve contextual understanding in AR environments. This study explores how machine learning enhances object and action recognition, improving context awareness in augmented reality environments. A systematic review of fifteen studies revealed advancements in deep learning, semantic web, knowledge graphs, and multimodal interfaces, which collectively improved the capabilities of augmented reality systems. The findings include innovations in aspects such as 3D hand pose estimation, semantic gaze analysis through eye tracking, and hybrid approaches that optimize real-time processing. These developments improve augmented and mixed reality environments by enabling more realistic, interactive, and responsive applications. However, persistent challenges such as latency, scalability, and environmental adaptation underscore the need for more research. These results suggest a guide for the development of highly immersive and efficient AR/MR systems, with the potential to improve user interaction and operational capabilities.