## 3 Result
### Preprocessing and Data Analysis  
Since the EMG signal based on IR was recorded, the EMG signal included the ES signal. For this reason, the ES signal disturbed the signal analysis and caused the degradation of the proposed algorithm. We applied preprocessing using the method described in Section 2.1, and the ES signal in the EMG signal was thus removed, as shown in Figure 6. As shown in Figure 6, we confirmed that the ES signal of the noisy signal with an averaged \(-\)14 dB signal-to-noise ratio (SNR) was almost suppressed, and the non-voluntary and voluntary muscle contraction signal remained, with very little residual noise. At this time, to calculate the SNR of the signal, the epoch length was set as only 1000 samples from the front. The SNR of the noisy signal at from 10 Hz to 45 Hz ES appeared with an average of \(-\)12 dB, and the SNR of noisy signal at from 50 Hz to 90 Hz ES appeared with an average of \(-\)17 dB. However, the signal which was suppressed by the ES signal included the VNVMC signal, and we have to classify the VNVMC signal to correctly measure the MQ, which was estimated using only non-voluntary MC. As shown in Figure 7, when using 10 Hz ES, we can distinguish the voluntary MC signal from the non-voluntary MC signal through amplitude discrimination. However, when using the 90 Hz ES, we cannot distinguish between two MC signals through the amplitude discrimination, as shown in Figure 7. Indeed, when the muscle was stimulated by high frequencies, exceeding 20 Hz, it was difficult to detect the voluntary MC signal using the amplitude discrimination. Therefore, after applying the ESS, we extracted the feature vector to classify the VNVMC signal in detail, using the LSTM model.  
Figure 5: Equipment for experiment. (**a**) Device for the ES, (a-1) Main device, (a-2) Electrode for the EMG, (a-3) Electrode for the ES, (**b**) Electrode pads, (b-1) Electrode for the ES and EMG, and (b-2) Electrode for reference.  
Figure 6: Result of the ESS-preprocessing method. (**a**) Entire plot of the result, (**b**) Magnified plot of the result.  
The feature vector was extracted using the ES-suppressed signal and consisted of PoSCS (19 dimension) and SCSyV (256 dimension). To confirm the availability of the extracted features, we compared the distribution of the feature from the non-voluntary MC signal with that from the mixed MC signal, as shown in Figure 8. As a result, the distributions of the feature between the VNVMC were distinctly separated. Thus, we confirmed that the extracted feature vector had useful characteristics to classify the VNVMC signal. However, when we variously set the frequency parameter of the ES in accordance with the type of the IR signal, the key features appeared differently to each parameter to classify the VNVMC signal. Since the key features had to be employed differently, according to muscle condition, it is difficult to classify the VNVMC using a threshold of the feature. For this reason, we need to train the LSTM model using variously extracted features to improve the performance of the algorithm. The feature vector was reconstructed using feature selection to improve the proposed algorithm. Finally, the input features of the proposed algorithm were experimentally selected for higher performance.  
Figure 7: EMG waveform after applying the ESS. (**a**) waveform by 10 Hz ES, (**b**) waveform by 90 Hz ES.  
Figure 8: Probability density function (PDF) of the extracted feature. (**a**) PDF in the 10 Hz ES environment, (**b**) PDF in the 60 Hz ES environment, (**c**) PDF in the 90 Hz ES environment.