Fall Detection using a Gaussian Distribution of Clustered Knowledge, Augmented Radial Basis Neural-Network, and Multilayer Perceptron

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dc.contributor.author Yuwono Mitchell en_US
dc.contributor.author Su Steven en_US
dc.contributor.author Moulton Bruce en_US
dc.contributor.editor Klempous, R en_US
dc.date.accessioned 2012-10-12T03:37:12Z
dc.date.available 2012-10-12T03:37:12Z
dc.date.issued 2011 en_US
dc.identifier 2011004459 en_US
dc.identifier.citation Yuwono Mitchell, Su Steven, and Moulton Bruce 2011, 'Fall Detection using a Gaussian Distribution of Clustered Knowledge, Augmented Radial Basis Neural-Network, and Multilayer Perceptron', , IEEE Explore, http://ieeexplore.ieee.org, , pp. 145-150. en_US
dc.identifier.issn NA en_US
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/19419
dc.description.abstract The rapidly increasing population of elderly people has posed a big challenge to research in fall prevention and detection. Substantial amounts of injuries, disabilities, traumas and deaths among elderly people due to falls have been reported worldwide. There is therefore a need for a reliable, simple, and affordable automatic fall detection system. This paper proposes a reliable fall detection algorithm using minimal information from a single waist worn wireless tri-axial accelerometer. The method proposed is to approach fall detection using digital signal processing and neural networks. This method includes the application of Discrete Wavelet Transform (DWT), Regrouping Particle Swarm Optimization (RegPSO), a proposed method called Gaussian Distribution of Clustered Knowledge (GCK), and an Ensemble of Classifiers using two different classifiers: Multilayer Perceptron Neural Network (MLP) and Augmented Radial Basis Neural Networks (ARBF). The proposed method has been tested on 8 healthy individuals in a home environment and yields promising result of up to 100% sensitivity on ingroup, 97.65% sensitivity on outgroup, and 99.56% specificity on Activities of Daily Living (ADL) data. en_US
dc.language en_US
dc.publisher IEEE Explore en_US
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6217909 en_US
dc.title Fall Detection using a Gaussian Distribution of Clustered Knowledge, Augmented Radial Basis Neural-Network, and Multilayer Perceptron en_US
dc.parent Proceedings of the 6th International Conference on Broadband Communications & Biomedical Applications en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation http://ieeexplore.ieee.org en_US
dc.identifier.startpage 145 en_US
dc.identifier.endpage 150 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 170205 en_US
dc.personcode 112628;997723;010755 en_US
dc.percentage 000100 en_US
dc.classification.name Neurocognitive Patterns and Neural Networks en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom 6th International Conference on Broadband Communications & Biomedical Applications en_US
dc.date.activity 20111121 en_US
dc.location.activity Melbourne en_US
dc.description.keywords Fall Detection; Discrete Wavelet Transform; Regrouping Particle Swarm Optimization; Gaussian Distribution of Clustered Knowledge; Ensemble of Classifiers; Augmented Radial Basis Neural Networks; en_US
dc.staffid en_US


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