Multi-modal Fall Detection

Multi-Modal Fall Detection is a fall detection framework that integrates data from multiple heterogeneous sensing modalities—such as inertial, visual, audio, or environmental sensors—to improve the accuracy and robustness of fall identification by leveraging complementary information across modalities.

18

Publications

1K

Citations

66

Authors

34

Institutions

Publications per year

2017–2026

10

Authors

66

  1. TT
    Thanh-Hai Tran

    Hanoi University of Science and Technology, Vietnam

    2 publications · 98 citations

    2

    h-index

  2. YE
    Yunus Emre Kara

    Boğaziçi University, Türkiye

    2 publications · 25 citations

    2

    h-index

  3. VH
    Van-Nam Hoang

    Hanoi University of Science and Technology, Vietnam

    2 publications · 98 citations

    2

    h-index

  4. HA
    Hande Alemdar

    Boğaziçi University, Türkiye

    2 publications · 25 citations

    2

    h-index

  5. CE
    Cem Ersoy

    Boğaziçi University, Türkiye

    2 publications · 25 citations

    2

    h-index

1–5 of 66

Venues

  1. Sensors

    2 publications · 450 citations

    2

    h-index

  2. 1

    h-index

  3. HAL (Le Centre pour la Communication Scientifique Directe)

    Repository · 1 publications · 24 citations

    1

    h-index

  4. Procedia Computer Science

    1 publications · 12 citations

    1

    h-index

  5. International Journal of Systems Science

    1 publications · 67 citations

    1

    h-index

1–5