Skeleton-based Fall Detection

Skeleton-Based Fall Detection is a fall detection method that utilizes human skeletal joint coordinates derived from depth or RGB video as the primary input, distinguishing itself from appearance-based or device-centric approaches by focusing exclusively on articulated body pose dynamics for identifying fall events.

49

Publications

2.3K

Citations

146

Authors

73

Institutions

Publications per year

2017–2026

40

Authors

146

  1. WM
    Weidong Min

    Nanchang University, China

    3 publications · 121 citations

    3

    h-index

  2. NM
    Nadia Magnenat‐Thalmann

    University of Geneva, Switzerland

    2 publications · 285 citations

    2

    h-index

  3. GF
    Gonzalo Farías

    Universidad Nacional de Educación a Distancia, Spain

    2 publications · 183 citations

    2

    h-index

  4. RA
    Rami Alazrai

    German Jordanian University, Jordan

    2 publications · 62 citations

    2

    h-index

  5. AR
    Anitha Rani Inturi

    SRM University, Andhra Pradesh, India

    2 publications · 91 citations

    2

    h-index

1–5 of 146

Venues

  1. IEEE Access

    4 publications · 284 citations

    4

    h-index

  2. Sensors

    4 publications · 82 citations

    4

    h-index

  3. Applied Sciences

    3 publications · 191 citations

    3

    h-index

  4. IET Image Processing

    1 publications · 19 citations

    1

    h-index

  5. 1

    h-index

1–5