Children Abnormal GAIT Classification Using Extreme Learning Machine

Dr. M.Pushpa Rani; G.Arumugam

Children Abnormal GAIT Classification Using Extreme Learning Machine

Dr. M.Pushpa Rani * §

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G.Arumugam

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§ Mother Teresa Women's University

Children Abnormal GAIT Classification Using Extreme Learning Machine

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Abstract

Analyzing human gait has earned considerable interest in recent computer vision researches, as it has immense use in deducing the physical well-being of people. Detection of unusual movement patterns can be performed using Support Vector Machines classification with T-Test pre-normalization. Support Vector Machine classifiers are powerful tools, specifically designed to solve large-scale classification problems. Almost all recent works broadly uses SVM method for gait analysis because of its remarkable learning ability. But when dealing with time complexity there exists a limitation with the SVM. As the computation cost for the SVM is high, the recently developed Extreme Learning Machine (ELM) is being used for the gait classification as a better option in this paper . ELM avoids problems like local minima, improper learning rate and over fitting commonly faced by previous iterative learning methods and completes the training very fast. The multi category classification performance of ELM with T-Test is evaluated with the Virginia gait dataset. The results indicate that ELM produces better classification accuracies with reduced training time and implementation complexity when compared to SVM.

References

26 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. M.Pushpa Rani, G.Arumugam. 1970. "Children Abnormal GAIT Classification Using Extreme Learning Machine". Global Journal of Computer Science and Technology GJCST Volume 10 (GJCST Volume 10 Issue 13).

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GJCST Volume 10 Issue 13

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Submission ReceivedFebruary 5, 2010
Peer Review Double Blind
Handling Editor
Accepted March 5, 2010
Published March 15, 2010

Version of record

v1.2

Issue date

October 12, 2010

Language

English

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Article Matrices

Total Score: 147

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Dr. M.Pushpa Rani, G.Arumugam (PhD/Dr. count: 1)

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Publish Date: 2010 03, Mon

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