A simple strategy for fall events detection

Type
Conference Paper

Authors
Harrou, Fouzi
Zerrouki, Nabil
Sun, Ying
Houacine, Amrane

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Statistics Program

KAUST Grant Number
OSR-2015-CRG4-2582

Online Publication Date
2017-01-20

Print Publication Date
2016-07

Date
2017-01-20

Abstract
The paper concerns the detection of fall events based on human silhouette shape variations. The detection of fall events is addressed from the statistical point of view as an anomaly detection problem. Specifically, the paper investigates the multivariate exponentially weighted moving average (MEWMA) control chart to detect fall events. Towards this end, a set of ratios for five partial occupancy areas of the human body for each frame are collected and used as the input data to MEWMA chart. The MEWMA fall detection scheme has been successfully applied to two publicly available fall detection databases, the UR fall detection dataset (URFD) and the fall detection dataset (FDD). The monitoring strategy developed was able to provide early alert mechanisms in the event of fall situations.

Citation
Harrou F, Zerrouki N, Sun Y, Houacine A (2016) A simple strategy for fall events detection. 2016 IEEE 14th International Conference on Industrial Informatics (INDIN). Available: http://dx.doi.org/10.1109/INDIN.2016.7819182.

Acknowledgements
This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.

Publisher
Institute of Electrical and Electronics Engineers (IEEE)

Journal
2016 IEEE 14th International Conference on Industrial Informatics (INDIN)

DOI
10.1109/INDIN.2016.7819182

Additional Links
http://ieeexplore.ieee.org/document/7819182/

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