Improved gender recognition during stepping activity for rehab application using the combinatorial fusion approach of EMG and HRV

Nor Aziyatul Izni Mohd Rosli, Mohd Azizi Abdul Rahman, Malarvili Balakrishnan, Takashi Komeda, Saiful Amri Mazlan, Hairi Zamzuri

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Gender recognition is trivial for a physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) (Hsu et al.) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SVM). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient and effective for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Barani's work (90.34%), Nazarloo's work (92.50%), and other classifiers.

Original languageEnglish
Article number348
JournalApplied Sciences (Switzerland)
Volume7
Issue number4
DOIs
Publication statusPublished - 2017 Mar 31

Keywords

  • Data fusion
  • Electromyography (EMG)
  • Feature fusion
  • Feature selection
  • Gender recognition
  • Heart Rate Variability (HRV)
  • Sensor fusion
  • Signal processing
  • Stepper

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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