Machine Learning For Fall Detection

Machine Learning for Fall Detection is a computational methodology that employs machine learning algorithms to automatically identify and classify human falls from sensor or video data, distinguished by its focus on modeling temporal-spatial patterns indicative of fall events within the broader domain of activity recognition systems.

528

Publications

25.8K

Citations

1.9K

Authors

687

Institutions

Publications per year

2017–2026

387

Authors

1.9K

  1. EC
    E. Casilari

    Universidad de Málaga, Spain

    7 publications · 339 citations

    7

    h-index

  2. WM
    Weidong Min

    Nanchang University, China

    6 publications · 285 citations

    6

    h-index

  3. MY
    Miao Yu

    Loughborough University, United Kingdom

    6 publications · 565 citations

    6

    h-index

  4. MG
    Moeness G. Amin

    Villanova University, United States

    5 publications · 551 citations

    5

    h-index

  5. SM
    Syed Mohsen Naqvi

    Newcastle University, United Kingdom

    5 publications · 547 citations

    5

    h-index

1–5 of 1.9K

Venues

  1. Sensors

    47 publications · 3.4K citations

    29

    h-index

  2. IEEE Access

    29 publications · 1.4K citations

    22

    h-index

  3. IEEE Sensors Journal

    16 publications · 1.3K citations

    14

    h-index

  4. IEEE Journal of Biomedical and Health Informatics

    14 publications · 1.7K citations

    14

    h-index

  5. Multimedia Tools and Applications

    11 publications · 444 citations

    11

    h-index

1–5