Fall Detection

Fall Detection is a computational process in humanoid robotics that identifies the unplanned loss of postural stability, distinguishing it from other motion anomalies by its abruptness and deviation from intended locomotion dynamics.

2.1K

Publications

131.3K

Citations

6.2K

Authors

1.8K

Institutions

Publications per year

2017–2026

971

Authors

6.2K

  1. MS
    Marjorie Skubic

    University of Missouri, United States

    25 publications · 2.3K citations

    20

    h-index

  2. NH
    Nigel H. Lovell

    UNSW Sydney, Australia

    24 publications · 2.7K citations

    19

    h-index

  3. MG
    Moeness G. Amin

    Villanova University, United States

    19 publications · 2K citations

    17

    h-index

  4. AB
    Alan Bourke

    University of Limerick, Ireland

    21 publications · 2.5K citations

    17

    h-index

  5. SJ
    Stephen J. Redmond

    UNSW Sydney, Australia

    19 publications · 1.1K citations

    15

    h-index

1–5 of 6.2K

Venues

  1. Sensors

    129 publications · 7.6K citations

    45

    h-index

  2. IEEE Sensors Journal

    58 publications · 4.8K citations

    41

    h-index

  3. IEEE Access

    58 publications · 2.8K citations

    31

    h-index

  4. 24

    h-index

  5. Expert Systems with Applications

    17 publications · 960 citations

    17

    h-index

1–5