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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Support vector machines for detecting age-related changes in running kinematics

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Author(s):
Fukuchi, Reginaldo K. [1] ; Eskofier, Bjoern M. [2] ; Duarte, Marcos [3] ; Ferber, Reed [1]
Total Authors: 4
Affiliation:
[1] Univ Calgary, Fac Kinesiol, Running Injury Clin, Calgary, AB T2N 1N4 - Canada
[2] Univ Calgary, Fac Kinesiol, Human Performance Lab, Calgary, AB T2N 1N4 - Canada
[3] Univ Sao Paulo, Sch Phys Educ & Sport, Sao Paulo - Brazil
Total Affiliations: 3
Document type: Journal article
Source: JOURNAL OF BIOMECHANICS; v. 44, n. 3, p. 540-542, FEB 3 2011.
Web of Science Citations: 29
Abstract

Age-related changes in running kinematics have been reported in the literature using classical inferential statistics. However, this approach has been hampered by the increased number of biomechanical gait variables reported and subsequently the lack of differences presented in these studies. Data mining techniques have been applied in recent biomedical studies to solve this problem using a more general approach. In the present work, we re-analyzed lower extremity running kinematic data of 17 young and 17 elderly male runners using the Support Vector Machine (SVM) classification approach. In total, 31 kinematic variables were extracted to train the classification algorithm and test the generalized performance. The results revealed different accuracy rates across three different kernel methods adopted in the classifier, with the linear kernel performing the best. A subsequent forward feature selection algorithm demonstrated that with only six features, the linear kernel SVM achieved 100% classification performance rate, showing that these features provided powerful combined information to distinguish age groups. The results of the present work demonstrate potential in applying this approach to improve knowledge about the age-related differences in running gait biomechanics and encourages the use of the SVM in other clinical contexts. (C) 2010 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 08/10461-7 - Posture and movement control of young and elderly sedentary individuals and runners
Grantee:Marcos Duarte
Support Opportunities: Regular Research Grants