Managing variability in the summary and comparison of gait data
Identifieur interne : 000338 ( Pmc/Curation ); précédent : 000337; suivant : 000339Managing variability in the summary and comparison of gait data
Auteurs : Tom Chau [Canada] ; Scott Young [Canada] ; Sue Redekop [Canada]Source :
- Journal of NeuroEngineering and Rehabilitation [ 1743-0003 ] ; 2005.
Abstract
Variability in quantitative gait data arises from many potential sources, including natural temporal dynamics of neuromotor control, pathologies of the neurological or musculoskeletal systems, the effects of aging, as well as variations in the external environment, assistive devices, instrumentation or data collection methodologies. In light of this variability, unidimensional, cycle-based gait variables such as stride period should be viewed as random variables and prototypical single-cycle kinematic or kinetic curves ought to be considered as random functions of time. Within this framework, we exemplify some practical solutions to a number of commonly encountered analytical challenges in dealing with gait variability. On the topic of univariate gait variables, robust estimation is proposed as a means of coping with contaminated gait data, and the summary of non-normally distributed gait data is demonstrated by way of empirical examples. On the summary of gait curves, we discuss methods to manage undesirable phase variation and non-robust spread estimates. To overcome the limitations of conventional comparisons among curve landmarks or parameters, we propose as a viable alternative, the combination of curve registration, robust estimation, and formal statistical testing of curves as coherent units. On the basis of these discussions, we provide heuristic guidelines for the summary of gait variables and the comparison of gait curves.
Url:
DOI: 10.1186/1743-0003-2-22
PubMed: 16053523
PubMed Central: 1208939
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<front><div type="abstract" xml:lang="en"><p>Variability in quantitative gait data arises from many potential sources, including natural temporal dynamics of neuromotor control, pathologies of the neurological or musculoskeletal systems, the effects of aging, as well as variations in the external environment, assistive devices, instrumentation or data collection methodologies. In light of this variability, unidimensional, cycle-based gait variables such as stride period should be viewed as random variables and prototypical single-cycle kinematic or kinetic curves ought to be considered as random functions of time. Within this framework, we exemplify some practical solutions to a number of commonly encountered analytical challenges in dealing with gait variability. On the topic of univariate gait variables, robust estimation is proposed as a means of coping with contaminated gait data, and the summary of non-normally distributed gait data is demonstrated by way of empirical examples. On the summary of gait curves, we discuss methods to manage undesirable phase variation and non-robust spread estimates. To overcome the limitations of conventional comparisons among curve landmarks or parameters, we propose as a viable alternative, the combination of curve registration, robust estimation, and formal statistical testing of curves as coherent units. On the basis of these discussions, we provide heuristic guidelines for the summary of gait variables and the comparison of gait curves.</p>
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<front><journal-meta><journal-id journal-id-type="nlm-ta">J Neuroengineering Rehabil</journal-id>
<journal-title>Journal of NeuroEngineering and Rehabilitation</journal-title>
<issn pub-type="epub">1743-0003</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
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<article-categories><subj-group subj-group-type="heading"><subject>Methodology</subject>
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<title-group><article-title>Managing variability in the summary and comparison of gait data</article-title>
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<contrib-group><contrib id="A1" corresp="yes" contrib-type="author"><name><surname>Chau</surname>
<given-names>Tom</given-names>
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<email>tom.chau@utoronto.ca</email>
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<aff id="I1"><label>1</label>
Bloorview MacMillan Children's Centre, Toronto, Canada</aff>
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Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada</aff>
<pub-date pub-type="collection"><year>2005</year>
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<pub-date pub-type="epub"><day>29</day>
<month>7</month>
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<history><date date-type="received"><day>30</day>
<month>4</month>
<year>2005</year>
</date>
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<month>7</month>
<year>2005</year>
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<permissions><copyright-statement>Copyright © 2005 Chau et al; licensee BioMed Central Ltd.</copyright-statement>
<copyright-year>2005</copyright-year>
<copyright-holder>Chau et al; licensee BioMed Central Ltd.</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0"></ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>
<pmc-comment>
Chau
Tom
tom.chau@utoronto.ca
Managing variability in the summary and comparison of gait data
2005 Journal of NeuroEngineering and Rehabilitation 2(1): 22-. (2005) 1743-0003(2005)2:1<22> urn:ISSN:1743-0003 </pmc-comment>
</license>
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<abstract><p>Variability in quantitative gait data arises from many potential sources, including natural temporal dynamics of neuromotor control, pathologies of the neurological or musculoskeletal systems, the effects of aging, as well as variations in the external environment, assistive devices, instrumentation or data collection methodologies. In light of this variability, unidimensional, cycle-based gait variables such as stride period should be viewed as random variables and prototypical single-cycle kinematic or kinetic curves ought to be considered as random functions of time. Within this framework, we exemplify some practical solutions to a number of commonly encountered analytical challenges in dealing with gait variability. On the topic of univariate gait variables, robust estimation is proposed as a means of coping with contaminated gait data, and the summary of non-normally distributed gait data is demonstrated by way of empirical examples. On the summary of gait curves, we discuss methods to manage undesirable phase variation and non-robust spread estimates. To overcome the limitations of conventional comparisons among curve landmarks or parameters, we propose as a viable alternative, the combination of curve registration, robust estimation, and formal statistical testing of curves as coherent units. On the basis of these discussions, we provide heuristic guidelines for the summary of gait variables and the comparison of gait curves.</p>
</abstract>
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