Statistical control chart and neural network classification for improving human fall detection

Fouzi Harrou, Nabil Zerrouki, Ying Sun, Amrane Houacine

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

4 Scopus citations

Abstract

This paper proposes a statistical approach to detect and classify human falls based on both visual data from camera and accelerometric data captured by accelerometer. Specifically, we first use a Shewhart control chart to detect the presence of potential falls by using accelerometric data. Unfortunately, this chart cannot distinguish real falls from fall-like actions, such as lying down. To bypass this difficulty, a neural network classifier is then applied only on the detected cases through visual data. To assess the performance of the proposed method, experiments are conducted on the publicly available fall detection databases: the University of Rzeszow's fall detection (URFD) dataset. Results demonstrate that the detection phase play a key role in reducing the number of sequences used as input into the neural network classifier for classification, significantly reducing computational burden and achieving better accuracy.
Original languageEnglish (US)
Title of host publication2016 8th International Conference on Modelling, Identification and Control (ICMIC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1060-1064
Number of pages5
ISBN (Print)9780956715777
DOIs
StatePublished - Jan 5 2017

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