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Appl. Sci. 2017, 7(4), 348; doi:10.3390/app7040348

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

1
Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
2
Department of Biotechnology and Medical Engineering, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
3
Department of Bioscience and Engineering, College of System Engineering, Shibaura Institute of Technology, Fukasaku 307, Saitama 337-8570, Japan
*
Author to whom correspondence should be addressed.
Received: 18 January 2017 / Revised: 27 March 2017 / Accepted: 27 March 2017 / Published: 31 March 2017
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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. View Full-Text
Keywords: signal processing; feature selection; feature fusion; data fusion; gender recognition; sensor fusion; Heart Rate Variability (HRV), Electromyography (EMG); Stepper signal processing; feature selection; feature fusion; data fusion; gender recognition; sensor fusion; Heart Rate Variability (HRV), Electromyography (EMG); Stepper
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Rosli, N.A.I.M.; Rahman, M.A.A.; Balakrishnan, M.; Komeda, T.; Mazlan, S.A.; Zamzuri, H. Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV. Appl. Sci. 2017, 7, 348.

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