TY - JOUR
T1 - Improved gender recognition during stepping activity for rehab application using the combinatorial fusion approach of EMG and HRV
AU - Rosli, Nor Aziyatul Izni Mohd
AU - Rahman, Mohd Azizi Abdul
AU - Balakrishnan, Malarvili
AU - Komeda, Takashi
AU - Mazlan, Saiful Amri
AU - Zamzuri, Hairi
N1 - Publisher Copyright:
© 2017 by the authors.
PY - 2017/3/31
Y1 - 2017/3/31
N2 - 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.
AB - 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.
KW - Data fusion
KW - Electromyography (EMG)
KW - Feature fusion
KW - Feature selection
KW - Gender recognition
KW - Heart Rate Variability (HRV)
KW - Sensor fusion
KW - Signal processing
KW - Stepper
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U2 - 10.3390/app7040348
DO - 10.3390/app7040348
M3 - Article
AN - SCOPUS:85017346611
VL - 7
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
SN - 2076-3417
IS - 4
M1 - 348
ER -