Gait Recognition Via Coalitional Game-based Feature Selection and Extreme Learning Machine

In order to achieve the goal of controlling the intelligent lower limb prosthesis effectively, it is very crucial to recognize the gait pattern of the lower limb, which usually includes walk, up and down stairs or slopes, etc. This paper proposes a gait recognition method based on coalitional game-b...

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Bibliographic Details
Published inNeuroQuantology Vol. 16; no. 2; p. 32
Main Authors Tian, Yiming, Chen, Wei, Li, Lifeng, Wang, Xitai, Liu, Zuojun
Format Journal Article
LanguageEnglish
Published Bornova Izmir NeuroQuantology 2018
Subjects
Algorithms
Feature extraction
Feature recognition
Gait recognition
Games
Machine learning
Neural networks
Production planning
Prostheses
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Summary:In order to achieve the goal of controlling the intelligent lower limb prosthesis effectively, it is very crucial to recognize the gait pattern of the lower limb, which usually includes walk, up and down stairs or slopes, etc. This paper proposes a gait recognition method based on coalitional game-based feature selection and extreme learning machine. Firstly, this paper extracts characteristic values of four periods in gait cycle, obtaining 24 features. Secondly, in order to improve the accuracy and reduce the computational complexity, a coalitional game-based feature selection algorithm is used to select the prominent features. Lastly, the extreme learning machine (ELM) is used to recognize the gait pattern, which can have a better result in identifying the five kinds of gait pattern in this experiment, compared with BP neural network. Compared with other feature selection algorithms, including mRMR and Relief-F, the proposed method selects fewer features and provides higher accuracy and has faster recognition speed, which proves the effectiveness and feasibility of the proposed method.
ISSN:1303-5150
1303-5150
DOI:10.14704/nq.2018.16.2.1173