The Novel Quantitative Technique for Assessment of Gait Symmetry Using Advanced Statistical Learning Algorithm.
The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 8 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas research Journal Article |
| Publicado: |
Wiley-Blackwell
2/2/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109273308&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109273308 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/2/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109273308 109273308 109273308 10.1155/2015/528971 109273308 ppf: 1 ppct: 7 formats: fmt: @attributes: type: P tig: atl: The Novel Quantitative Technique for Assessment of Gait Symmetry Using Advanced Statistical Learning Algorithm. aug: au: Wu, Jianning Wu, Bin affil: School of Mathematics and Computer Science, Fujian Normal University, Fuzhou 350007, China sug: subj: Gait Analysis Learning Human T-Tests Kinetics Data Collection Descriptive Statistics Statistics Funding Source ab: The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis. pubtype: Academic Journal doctype: algorithm equations & formulas research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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