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

Zhou, J; Su, SW; Guo, A

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

Zhou, J Su, SW

Guo, A

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Publisher:: WASET
Publication Type:: Conference Proceeding
Citation:: WASET: International Conference on Information, Communications and Signal Processing, 2012, pp. 729 - 734
Issue Date:: 2012-01

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Field	Value	Language
dc.contributor.author	Zhou, J	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Guo, A	en_US
dc.contributor.editor	Vaninsky, A	en_US
dc.contributor.editor	Bolotin, A	en_US
dc.date	2012-12-06	en_US
dc.date.issued	2012-01	en_US
dc.identifier.citation	WASET: International Conference on Information, Communications and Signal Processing, 2012, pp. 729 - 734	en_US
dc.identifier.issn	2010-376X	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.	en_US
dc.publisher	WASET	en_US
dc.relation.ispartof	WASET: International Conference on Information, Communications and Signal Processing	en_US
dc.relation.ispartof	International Conference on Information, Communications and Signal Processing	en_US
dc.title	Fault Detection and Identification of COSMED K4b2 based on PCA and Neural Network	en_US
dc.type	Conference Proceeding
utslib.location	Penang, Malaysia	en_US
utslib.location.activity	Penang, Malaysia	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Penang, Malaysia	en_US
pubs.embargo.period	Not known	en_US
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2012-12-09	en_US
pubs.place-of-publication	Penang, Malaysia	en_US
pubs.start-date	2012-12-06	en_US

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.

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http://hdl.handle.net/10453/23203