Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 25 March 2017 / Approved: 28 March 2017 / Online: 28 March 2017 (02:38:01 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Appl. Sci. 2017, 7, 348
DOI: 10.3390/app7040348

Abstract

Gender recognition is trivial for 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 the stepping 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) 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 (SMO). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient 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 Nazarloo's work (90.34%) and other classifiers.

Subject Areas

signal processing; feature selection; feature fusion; data fusion; gender recognition; sensor fusion; heart rate variability (HRV), electromyography (EMG); stepper

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