Video-based Fall Detection

Video-Based Fall Detection is a methodological framework within the domain of activity recognition that uses visual data from video sequences to automatically identify human falls by analyzing spatiotemporal motion patterns, body posture, and contextual scene information.

204

Publications

11.7K

Citations

627

Authors

258

Institutions

Publications per year

2017–2026

92

Authors

627

  1. JM
    Jean Meunier

    Université de Montréal, Canada

    9 publications · 1.5K citations

    9

    h-index

  2. JR
    Jacqueline Rousseau

    Université de Montréal, Canada

    7 publications · 1.4K citations

    7

    h-index

  3. IY
    Irene Yu‐Hua Gu

    Chalmers University of Technology, Sweden

    6 publications · 137 citations

    6

    h-index

  4. MY
    Miao Yu

    Loughborough University, United Kingdom

    6 publications · 255 citations

    6

    h-index

  5. BV
    Bart Vanrumste

    KU Leuven, Belgium

    6 publications · 208 citations

    6

    h-index

1–5 of 627

Venues

  1. Sensors

    6 publications · 402 citations

    6

    h-index

  2. IEEE Sensors Journal

    4 publications · 236 citations

    4

    h-index

  3. IEEE Access

    4 publications · 259 citations

    4

    h-index

  4. 4

    h-index

  5. Applied Sciences

    3 publications · 120 citations

    3

    h-index

1–5