Index finger motion recognition using self-advise support vector machine

Khairul Anam, Adel Al Jumaily, Yashar Maali

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Because of the functionality of an index finger, the disability of its motion in the modern age can decrease the person's quality of life. As a part of rehabilitation therapy, the recognition of the index finger motion for rehabilitation purposes should be done properly. This paper proposes a novel recognition system of the index finger motion suing a cutting-edge method and its improvements. The proposed system consists of combination of feature extraction method, a dimensionality reduction and well-known classifier, Support Vector Machine (SVM). An improvement of SVM, Self-advise SVM (SA-SVM), is tested to evaluate and compare its performance with the original one. The experimental result shows that SA-SVM improves the classification performance by on average 0.63%.
Original languageEnglish
Pages (from-to)644-657
Number of pages14
JournalInternational Journal on Smart Sensing and Intelligent Systems
Volume7
Issue number2
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Support Vector Machine
  • Self-advise SVM
  • Pattern recognition
  • Support vector machine

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