A Dictionary Learning Based Approach for Gait Classification

被引：0

作者：

Poschadel, Nils ^{[1
]}

Moghaddamnia, Sanam ^{[1
]}

Alcaraz, Javier Conte ^{[1
]}

Steinbach, Marc ^{[2
]}

Peissig, Juergen ^{[1
]}

机构：

[1] Leibniz Univ Hannover, Inst Commun Technol, Hannover, Germany

[2] Leibniz Univ Hannover, Inst Appl Math, Hannover, Germany

来源：

2017 22ND INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP) | 2017年

关键词：

Sparse coding; dictionary learning; gait classification;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

To foster diagnosis of gait abnormalities as well as tracking the recovery rate in the course of healing, automated gait classification methods have great added value. Therefore, gait classification based on a dictionary learning approach was developed and tested. With an average classification rate of about 93%, the proposed method offers great potential to be deployed in support of digital healthcare and therapy. Moreover, by providing efficient data storage as well as low runtime, it is ideal for use in portable diagnostic tools.

引用

页数：4

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