Sensor-based Fall Assessment

Sensor-Based Fall Assessment is a methodological framework that distinguishes itself from other fall assessment approaches by utilizing data from wearable or ambient sensors to objectively identify, analyze, and evaluate fall events and related biomechanical parameters in real time.

157

Publications

7.3K

Citations

621

Authors

265

Institutions

Publications per year

2017–2026

71

Authors

621

  1. BR
    Barry R. Greene

    University College Dublin, Ireland

    14 publications · 871 citations

    14

    h-index

  2. KD
    Kim Delbaere

    Neuroscience Research Australia, Australia

    7 publications · 316 citations

    7

    h-index

  3. SR
    Stephen R. Lord

    Neuroscience Research Australia, Australia

    7 publications · 323 citations

    7

    h-index

  4. EP
    Emer P. Doheny

    University College Dublin, Ireland

    7 publications · 345 citations

    7

    h-index

  5. RA
    Rose Anne Kenny

    Trinity College Dublin, Ireland

    6 publications · 559 citations

    6

    h-index

1–5 of 621

Venues

  1. Sensors

    18 publications · 502 citations

    15

    h-index

  2. Gait & Posture

    6 publications · 481 citations

    6

    h-index

  3. IEEE Transactions on Biomedical Engineering

    4 publications · 560 citations

    4

    h-index

  4. 4

    h-index

  5. BMC Geriatrics

    2 publications · 43 citations

    3

    h-index

1–5