On discovering the correlated relationship between static and dynamic data in clinical gait analysis

Song, Y; Zhang, J; Cao, L; Sangeux, M

On discovering the correlated relationship between static and dynamic data in clinical gait analysis

Song, Y Zhang, J

Cao, L

Sangeux, M

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8190 LNAI (PART 3), pp. 563 - 578
Issue Date:: 2013-10-31

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Field	Value	Language
dc.contributor.author	Song, Y	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Sangeux, M	en_US
dc.date.issued	2013-10-31	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8190 LNAI (PART 3), pp. 563 - 578	en_US
dc.identifier.isbn	9783642409936	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26555
dc.description.abstract	'Gait' is a person's manner of walking. Patients may have an abnormal gait due to a range of physical impairment or brain damage. Clinical gait analysis (CGA) is a technique for identifying the underlying impairments that affect a patient's gait pattern. The CGA is critical for treatment planning. Essentially, CGA tries to use patients' physical examination results, known as static data, to interpret the dynamic characteristics in an abnormal gait, known as dynamic data. This process is carried out by gait analysis experts, mainly based on their experience which may lead to subjective diagnoses. To facilitate the automation of this process and form a relatively objective diagnosis, this paper proposes a new probabilistic correlated static-dynamic model (CSDM) to discover correlated relationships between the dynamic characteristics of gait and their root cause in the static data space. We propose an EM-based algorithm to learn the parameters of the CSDM. One of the main advantages of the CSDM is its ability to provide intuitive knowledge. For example, the CSDM can describe what kinds of static data will lead to what kinds of hidden gait patterns in the form of a decision tree, which helps us to infer dynamic characteristics based on static data. Our initial experiments indicate that the CSDM is promising for discovering the correlated relationship between physical examination (static) and gait (dynamic) data. © 2013 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-40994-3_36	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	On discovering the correlated relationship between static and dynamic data in clinical gait analysis	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 3	en_US
utslib.citation.volume	8190 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Prague, Czech Republic	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.issue	PART 3	en_US
pubs.publication-status	Published	en_US
pubs.volume	8190 LNAI	en_US

Abstract:

'Gait' is a person's manner of walking. Patients may have an abnormal gait due to a range of physical impairment or brain damage. Clinical gait analysis (CGA) is a technique for identifying the underlying impairments that affect a patient's gait pattern. The CGA is critical for treatment planning. Essentially, CGA tries to use patients' physical examination results, known as static data, to interpret the dynamic characteristics in an abnormal gait, known as dynamic data. This process is carried out by gait analysis experts, mainly based on their experience which may lead to subjective diagnoses. To facilitate the automation of this process and form a relatively objective diagnosis, this paper proposes a new probabilistic correlated static-dynamic model (CSDM) to discover correlated relationships between the dynamic characteristics of gait and their root cause in the static data space. We propose an EM-based algorithm to learn the parameters of the CSDM. One of the main advantages of the CSDM is its ability to provide intuitive knowledge. For example, the CSDM can describe what kinds of static data will lead to what kinds of hidden gait patterns in the form of a decision tree, which helps us to infer dynamic characteristics based on static data. Our initial experiments indicate that the CSDM is promising for discovering the correlated relationship between physical examination (static) and gait (dynamic) data. © 2013 Springer-Verlag.

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http://hdl.handle.net/10453/26555