A simple strategy for fall events detection

Fouzi Harrou, Nabil Zerrouki, Ying Sun, Amrane Houacine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

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.
Original languageEnglish (US)
Title of host publication2016 IEEE 14th International Conference on Industrial Informatics (INDIN)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages332-336
Number of pages5
ISBN (Print)9781509028702
DOIs
StatePublished - Jan 20 2017

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