Multimodal Context Fusion

Multimodal Context Fusion is a context-aware fusion method that integrates heterogeneous contextual data from multiple modalities—such as visual, auditory, spatial, and physiological inputs—through synchronized feature-level or decision-level fusion to enhance situation awareness and adaptive response in augmented reality environments.

24

Publications

1.6K

Citations

106

Authors

57

Institutions

Publications per year

2017–2026

17

Authors

106

  1. EB
    Erik Blasch

    United States Air Force Research Laboratory, United States

    2 publications · 83 citations

    2

    h-index

  2. MG
    Maoning Ge

    Nagoya University, Japan

    1 publications · 36 citations

    1

    h-index

  3. AB
    Austin Botelho

    Hamedan University of Technology, Iran

    1 publications · 12 citations

    1

    h-index

  4. MR
    Martin Raubal

    ETH Zurich, Switzerland

    1 publications · 39 citations

    1

    h-index

  5. LJ
    Licheng Jiao

    Xidian University, China

    1 publications · 37 citations

    1

    h-index

1–5 of 106

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 47 citations

    3

    h-index

  2. IEEE Pervasive Computing

    1 publications · 10 citations

    1

    h-index

  3. Neurocomputing

    1 publications · 96 citations

    1

    h-index

  4. Knowledge-Based Systems

    1 publications · 37 citations

    1

    h-index

  5. IEEE Access

    1 publications · 117 citations

    1

    h-index

1–5