A Data-Driven Monitoring Technique for Enhanced Fall Events Detection

Nabil Zerrouki, Fouzi Harrou, Ying Sun, Amrane Houacine

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Fall detection is a crucial issue in the health care of seniors. In this work, we propose an innovative method for detecting falls via a simple human body descriptors. The extracted features are discriminative enough to describe human postures and not too computationally complex to allow a fast processing. The fall detection is addressed as a statistical anomaly detection problem. The proposed approach combines modeling using principal component analysis modeling with the exponentially weighted moving average (EWMA) monitoring chart. The EWMA scheme is applied on the ignored principal components to detect the presence of falls. Using two different fall detection datasets, URFD and FDD, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional PCA-based methods.
Original languageEnglish (US)
Pages (from-to)333-338
Number of pages6
Issue number5
StatePublished - Jul 26 2016

Bibliographical note

KAUST Repository Item: Exported on 2023-08-04
Acknowledged KAUST grant number(s): OSR-2015-CRG4-258
Acknowledgements: This publication is based upon work supported by the King Ab-dullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No:OSR-2015-CRG4-2582.


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