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XR Content Reliability Enhancement Framework Based on Multi-modal Sensor Fusion

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2026, 12(3), 13
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : May 4, 2026
  • Accepted : June 22, 2026
  • Published : June 30, 2026

Jung Soo Han 1

1백석대학교

Accredited

ABSTRACT

This paper investigates methods for enhancing the reliability of XR content based on multimodal sensor fusion, focusing on reliability concepts, sensor fusion architectures, data verification frameworks, and user feedback mechanisms. The results indicate that XR content reliability is primarily determined by three key factors: accuracy, consistency, and integrity, and that even minor errors can lead to safety risks in industrial environments. To address these challenges, multimodal sensor fusion technologies integrating LiDAR, cameras, IMUs, and environmental sensors play a critical role. Furthermore, techniques such as noise reduction, time synchronization, Kalman filtering, graph optimization, and deep learning-based cross-modal fusion contribute significantly to improving content accuracy and system stability. In addition, anomaly detection and user-response-based interfaces were found to enhance both system reliability and perceived trustworthiness. In conclusion, multimodal sensor fusion is a core technology for ensuring XR content reliability, and future XR systems are expected to evolve into intelligent context-aware platforms integrating sensing, decision-making, and feedback processes.

Citation status

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