Real-time Fall Detection

Real-Time Fall Detection is a computational method that enables the immediate identification of fall incidents as they occur, distinguishing itself from delayed or retrospective approaches by providing continuous, low-latency monitoring and alarm generation within a safety-critical timeframe.

224

Publications

10.2K

Citations

813

Authors

334

Institutions

Publications per year

2017–2026

115

Authors

813

  1. GF
    Giancarlo Fortino

    University of Calabria, Italy

    3 publications · 153 citations

    3

    h-index

  2. JM
    Jean Meunier

    Université de Montréal, Canada

    3 publications · 390 citations

    3

    h-index

  3. AR
    Amanda Rodriguez

    Texas Tech University Health Sciences Center, United States

    3 publications · 137 citations

    3

    h-index

  4. AL
    Alessandro Leone

    Institute for Microelectronics and Microsystems, Italy

    3 publications · 151 citations

    3

    h-index

  5. SZ
    Steven Zupancic

    Texas Tech University Health Sciences Center, United States

    3 publications · 137 citations

    3

    h-index

1–5 of 813

Venues

  1. Sensors

    8 publications · 351 citations

    8

    h-index

  2. Procedia Computer Science

    5 publications · 177 citations

    5

    h-index

  3. IEEE Access

    5 publications · 271 citations

    5

    h-index

  4. Image and Vision Computing

    3 publications · 156 citations

    3

    h-index

  5. Journal of Real-Time Image Processing

    3 publications · 85 citations

    3

    h-index

1–5