## 3 Materials and Methods
### Gesture Recognition Method Using EMG Signals  
For the two-step biometrics methods proposed herein, gesture recognition was performed as shown in Figure 4. Training and classification were performed with an LSTM network after extracting five time domain features from the preprocessed EMG signal, and the time domain features used in the experiment were mean absolute value (MAV), variance (VAR), integrated EMG (IEMG), MAV slope (MAVSLP), and waveform length (WL). MAV can detect muscle contraction, which is a feature widely used in control applications using EMG signals. VAR uses the power of the EMG signal as a feature, representing the power information of the EMG signal. IEMG is a feature used as an index to detect muscle activity, representing the force applied to a muscle. MAVSLP is an improved feature of MAV that can be calculated using the difference between adjacent MAVs, and WL is a feature representing the difference in the length of the waveform of the EMG signal. The  
Figure 3: Comparison before and after EMG signal preprocessing in time-frequency domain of a notch filter (NF) and a band pass filter (BPF).  
Figure 2: Convolutional neural network-long short-term memory (CNN-LSTM) network-based two-step biometrics flowchart.  
five time domain features can be calculated using the formula shown in Figure 4, where \(x_{i}\) denotes the EMG signal and \(N\) denotes the signal length [25; 26].  
Training and classification were performed using an LSTM network after the features were extracted from the time domain of the EMG signal. The LSTM network is characterized by maintaining sequence information in the time domain, thereby an important role in improving classification accuracy [27]. In addition, the LSTM network is a structure that compensates for the disadvantage of the vanishing gradient problem of RNN. Four gates (forget, input, output, updating) can be used to selectively transfer or delete information from sequential inputs to maintain dependencies in the sequence, as shown in Figure 5.  
The LSTM network had two transfer parameters, \(c(t)\) and \(h(t)\), calculated by using four gates. The forget gate was calculated using Equation (1), and the input gate was calculated using Equation (2). Parameters \(c(t)\) and \(h(t)\) to be transferred to the next LSTM network were calculated using Equations (3) and (4), respectively. In the equation, \(\sigma\) denotes the sigmoid function, \(*\) denotes the convolution, and \(W\) denotes the weight. In this paper, we performed gesture recognition using an LSTM network consisting of four LSTM layers and two fully connected layers. The layer placement and parameters of the network were selected through experiments, and the designed network deployed one dropout (0.3 probability) for two LSTM networks. An LSTM network uses a many-to-one structure that receives the time domain characteristics of a multi EMG channel as input and outputs the gesture class. Network learning is done by setting adam optimizer, hidden unit 512 and batch size 128, and epoch with 100.  
\[f_{t}=\sigma\big{(}W_{xf}x_{t}+W_{hf}h_{t-1}+b_{f}\big{)} \tag{1}\]  
\[i_{t}=\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+b_{i}) \tag{2}\]  
\[c_{t}=f_{t}*c_{t-1}+i_{t}*\tanh(W_{xc}x_{t}+W_{hc}h_{t-1}+b_{c}) \tag{3}\]  
\[h_{t}=\sigma(W_{xh}x_{t}+W_{hh}h_{t-1}+b_{h}) \tag{4}\]  
Figure 4: Flowchart of gesture recognition using time domain features and the LSTM network.  
Figure 5: Basic LSTM network structure.