### Gender Recognition Using Physiological Signal  
Meanwhile, other information from physiological biometrics that reflects neurological changes such as electromyography (EMG) and autonomic behavior such as heart rate variability (HRV) can also affect gender recognition. Gender recognition using new physiological biometrics (e.g., EMG, ECG, and EEG) is quite complicated compared to face, iris and fingerprint physiological biometrics. This is because the signal of ECG and EMG recordings can be changed or distorted due to the physical status of the subject, movements, powerline interference and certain medications [20; 21]. However, the EMG and ECG biometrics information are difficult to be stolen and impossible to imitate. So, it is well secured and confidential [22]. Much basic research on electrocardiogram (ECG) has been actively conducted. Several suggestions proposed the feasibility of utilizing the ECG as another biometric modality for identity identification [23; 24]. The fact that the physiological differences of the heart in various people show certain uniqueness in their ECG signals have supported why it is logical for biometric recognition [25]. Gender recognition indicated by HRV is a standout amongst the most challenging issues in individual identification [26]. Furthermore, digital information about the physical muscles is provided by EMG. Although there a few analysis on EMG involving the lower limb [27], a research work on gender recognition during the stepping activity using EMG is yet to be investigated. Interestingly, there is a significant time delay difference between genders during the step [28], which indicates that there must be some relevant features in human muscles that help in differentiating gender. Multimodal physiological biometrics are a combination of at least two physiological biometric features in order to handle certain limitations of utilizing them separately. There have been some previous attempts to fuse multiple biometrics such as combining the face, gait, fingerprint, palm print and geometry for gender recognition [29; 30; 31]. Table 2 shows more related works for the fusion of physiological biometrics and the gap of this study. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Work** & **Method** \\ \hline
[7] & \(\bullet\) Sift features \\
[8] & \(\bullet\) Combination of gabor filters and binary features \\
[9] & \(\bullet\) Using hybrid of gabor filters and binary features \\
[10] & \(\bullet\) Face, based gender recognition performance with a fuzzy inference system \\
[11] & \(\bullet\) Using periocular biometric for gender classification in the wild \\
[12] & \(\bullet\) From gait, based using mixed conditional random field \\
[13] & \(\bullet\) Extraction of the hip joint data that was computed from Bio-vision hierarchical data \\
[14] & \(\bullet\) From gait sequences with arbitrary walking directions \\
[15] & \(\bullet\) Human gait based gender identification using Hidden Markov Model and Support Vector Machines \\
[16] & \(\bullet\) Using footwear appearance \\
[17] & \(\bullet\) Color information \\
[18; 19] & \(\bullet\) Speech recognition \\ This research & \(\bullet\) Fusion of HRV and EMG signals during stepping exercise by stepper \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Most related works for gender identification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work** & \multicolumn{3}{c}{**Physiological Signals Fusion**} & **Application** \\ \hline  & **ECG** & **EMG** & **EEG** & \\ \hline
[32; 33; 34] & ✓ & & ✓ & Automatic seizure detection \\
[35] & & ✓ & ✓ & hybrid-BCI \\
[36] & ✓ & & ✓ & Epileptic event detection \\
[37] & & ✓ & ✓ & Human movement intention \\
[38] & ✓ & & ✓ & Automatic sleep stage classification \\
[39] & & ✓ & ✓ & Gait intent detection \\
[40; 41] and this research & ✓ & ✓ & & Gender Identification for rehabilitation exercise \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Most related work for the fusion of physiological signals.