Self-Organizing Maps for the Analysis of Complex Movement Patterns

被引：0

作者：

H.U. Bauer

W. Schöllhorn

机构：

[1] Max-Planck-Institut für Strömungsforschung,SFB 185 ‘Nichtllineare Dynamik’

[2] JWG-Universität,Institut für Biomechanik

[3] JWG-Universität,undefined

来源：

Neural Processing Letters | 1997年 / 5卷

关键词：

cluster analysis; dimension-reduction; movement pattern; neighborhood preservation; SOM;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

We apply the Self-Organizing-Map-algorithm (SOM) as a central processing step in a new scheme for the characterisation of movement patterns of athletes. Due to its non-linear dimension reduction capabilities, the SOM outperforms a direct processing of the data as well as preprocessing using principal component analysis. Our results open the way to an objective assessment of movement patterns, with possible applications in the sport sciences as well as in medicine.

引用

页码：193 / 199

页数：6

共 6 条

[1]

Holzreiter S.H.(1993)Assessment of gait patterns using neural networks J. Biomech. 26 645-651

[2]

Köhle M.E.(1992)Quantifying the neighbourhood preservation of Self-Organizing Feature Maps IEEE Trans. on Neural Netw. 3 570-579

[3]

Bauer H.-U.(1995)An index of topology preservation for feature extraction Patt. Rec. 28 381-391

[4]

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[5]

Bezdek J.C.(undefined)undefined undefined undefined undefined-undefined

[6]

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