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

Handle URI:
http://hdl.handle.net/10754/622645
Title:
Statistical control chart and neural network classification for improving human fall detection
Authors:
Harrou, Fouzi; Zerrouki, Nabil; Sun, Ying ( 0000-0001-6703-4270 ) ; Houacine, Amrane
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Harrou F, Zerrouki N, Sun Y, Houacine A (2016) Statistical control chart and neural network classification for improving human fall detection. 2016 8th International Conference on Modelling, Identification and Control (ICMIC). Available: http://dx.doi.org/10.1109/ICMIC.2016.7804269.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2016 8th International Conference on Modelling, Identification and Control (ICMIC)
KAUST Grant Number:
OSR-2015-CRG4-2582
Issue Date:
5-Jan-2017
DOI:
10.1109/ICMIC.2016.7804269
Type:
Conference Paper
Sponsors:
This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR- 2015-CRG4-2582.
Additional Links:
http://ieeexplore.ieee.org/document/7804269/
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorZerrouki, Nabilen
dc.contributor.authorSun, Yingen
dc.contributor.authorHouacine, Amraneen
dc.date.accessioned2017-01-09T06:09:06Z-
dc.date.available2017-01-09T06:09:06Z-
dc.date.issued2017-01-05en
dc.identifier.citationHarrou F, Zerrouki N, Sun Y, Houacine A (2016) Statistical control chart and neural network classification for improving human fall detection. 2016 8th International Conference on Modelling, Identification and Control (ICMIC). Available: http://dx.doi.org/10.1109/ICMIC.2016.7804269.en
dc.identifier.doi10.1109/ICMIC.2016.7804269en
dc.identifier.urihttp://hdl.handle.net/10754/622645-
dc.description.abstractThis 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.en
dc.description.sponsorshipThis publication is based upon work supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR- 2015-CRG4-2582.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7804269/en
dc.rights(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectAccelerationen
dc.subjectBiological neural networksen
dc.subjectCamerasen
dc.subjectControl chartsen
dc.subjectFeature extractionen
dc.subjectMonitoringen
dc.titleStatistical control chart and neural network classification for improving human fall detectionen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journal2016 8th International Conference on Modelling, Identification and Control (ICMIC)en
dc.eprint.versionPost-printen
dc.contributor.institutionUniversity of Sciences and Technology Houari Boum├ędienne Algeria, LCPTS, Faculty of Electronics and Computer Scienceen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
kaust.grant.numberOSR-2015-CRG4-2582en
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