Fault Detection and Identification of COSMED K4b2 based on PCA and Neural Network

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dc.contributor.author Zhou, J
dc.contributor.author Su, SW
dc.contributor.author Guo, A
dc.contributor.editor Vaninsky, A
dc.contributor.editor Bolotin, A
dc.date.accessioned 2013-06-28T02:16:57Z
dc.date.issued 2012-01
dc.identifier.citation WASET:International conference on Information, communication and Signal Processing, 2012, pp. 729 - 734
dc.identifier.issn 2010-376X
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/23203
dc.description.abstract COSMED K4b2 is a portable electrical device designed to test pulmonary functions. It is ideal for many applications that need the measurement of the cardio-respiratory response either in the field or in the lab is capable with the capability to delivery real time data to a sink node or a PC base station with storing data in the memory at the same time. But the actual sensor outputs and data received may contain some errors, such as impulsive noise which can be related to sensors, low batteries, environment or disturbance in data acquisition process. These abnormal outputs might cause misinterpretations of exercise or living activities to persons being monitored. In our paper we propose an effective and feasible method to detect and identify errors in applications by principal component analysis (PCA) and a back propagation (BP) neural network.
dc.format Shannon Brown
dc.publisher WASET
dc.title Fault Detection and Identification of COSMED K4b2 based on PCA and Neural Network
dc.type Conference Proceeding
dc.parent WASET:International conference on Information, communication and Signal Processing
dc.journal.number en_US
dc.publocation Penang, Malaysia en_US
dc.publocation Penang, Malaysia
dc.identifier.startpage 729 en_US
dc.identifier.endpage 734 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.conference International conference on Information, communication and Signal Processing
dc.conference International conference on Information, communication and Signal Processing
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 997723
dc.personcode 116378
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International conference on Information, communication and Signal Processing en_US
dc.date.activity 20121206 en_US
dc.date.activity 2012-12-06
dc.date.activity 2012-12-06
dc.location.activity Penang, Malaysia en_US
dc.location.activity Penang, Malaysia
dc.description.keywords BP Neural Network, Exercising Testing, Fault Detection and Identification, Principal Component Analysis.
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Elec, Mech and Mechatronic Systems
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies
pubs.organisational-group /University of Technology Sydney/Students
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true
utslib.collection.history Closed (ID: 3)


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