NDLI: Gait Classificaiton in Children with Cerebral Palsy by Bayesian Approach

Content Provider	IEEE Xplore Digital Library
Author	Bai-ling Zhang Yanchun Zhang Pham, T.D. Begg, R.K.
Copyright Year	2007
Description	Author affiliation: Victoria Univ., Melbourne (Bai-ling Zhang; Yanchun Zhang)
Abstract	Cerebral palsy (CP) is generally considered as a nonprogressive neuro-developmental condition that occurs in early childhood and is associated with a motor impairment, usually affecting mobility and posture. Automatic accurate identification of cerebral palsy gait has many potential applications, for example, assistance in diagnosis, clinical decisionmaking and communication among the clinical professionals. In previous studies, support vector machine (SVM) and neural networks have been applied to classify CP gait patterns. However, one of the disadvantages of SVM and many neural network models is that given a gait sample, it only predicts a gait pattern class label without providing any estimate of the underlying probability, which is particularly important in Computer Aided Diagnostics applications. The objective of this study is to first investigate different pattern classification paradigms in the automatic gait analysis and address the significance of Bayesian classifier model, and then give a comprehensive performances comparison. Using a publicly available CP gait dataset (68 normal healthy and 88 with spastic diplegia form of CP), different features including the two basic temporal-spatial gait parameters (stride length and cadence) have been experimented. Various hold-out and cross-validation testing show that the Bayesian model offers excellent classification performances compared with some popular classifiers such as random forest and multiple layer perceptron. With many advantages considered, Bayesian classifier model is very significant in establishing a clinical decision system for gait analysis.
Starting Page	651
Ending Page	655
File Size	287579
Page Count	5
File Format	PDF
ISBN	9781424415014
DOI	10.1109/ISSNIP.2007.4496920
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-03
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Bayesian methods Neural networks Support vector machine classification Pattern classification Computer applications Birth disorders Predictive models Computer networks Application software
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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