## 3 Result
### Data Collection Protocol and Data Sets  
Every participant signed an informed consent before the measurement. For this experiment, as shown in Figure 5a, we employed the ES device (ExoRehab, Exosystems, Gyeonggi-do, Korea), which was approved as a medical device by the Korean government. In addition, we used the electrode pads [width: 90 mm, height: 50 mm, contact surface component: hydrogel, resistance: 50 ohms per 20 mm] (StiMus Electrode, HUREV Corp., Gangwon-do, Korea), as shown in Figure 5b. We collected the EMG data from two subjects using our device, as shown in Figure 5a,b. To record the EMG signal, which included the VNVMC signal, we attached the electrode pad to the right thigh, as shown in Figure 1. Then, the reference electrode, which was cut as in (b-2) of Figure 5b, was attached to knee. At this time, since the surface area of the electrode pad (b-1) of Figure 5b was relatively larger than that of the knee, the electrode pad might be separated from the knee while moving the muscle. Therefore, to stably collect the EMG data, we employed a pad of suitable size such as the electrode pad (b-2) in Figure 5b. To collect the VNVMC signal using a varying frequency, which was set from 10 Hz to 90 Hz, we repeatedly collected the data while the parameter increased by 5 Hz. The amplitude of the ES was set 22.10 V\({}_{\text{PP}}\) or 14.72 V\({}_{\text{PP}}\) according to the endurance of the subjects in terms of the pain. While the device was turned on for the ES during 35 s, the only non-voluntary MC signal was recorded for 15 s without the voluntary MC. Next, the non-voluntary MC signal and the voluntary MC signal were simultaneously collected while the muscle of the subject spontaneously contracted for 10 s. Then, the non-voluntary MC signal was obtained for 10 s without the voluntary MC. Since we allowed the participants to sit for one minute before beginning each signal collection to rest, muscle fatigue was minimized while collecting the DB. Finally, we obtained a database (DB) for about 6510 s for six subjects (height (mean \(\pm\) SD): 167.0 \(\pm\) 6.5 cm, weight: 67.2 \(\pm\) 8.9 kg, age: 31.0 \(\pm\) 8.4 years, # of male/female: 4/2) and the DB was divided into the training DB and test DB to conduct the experiment. Although the experiment was performed with a few participants, the number of the subjects that participated in our experiment was similar when comparing some studies using EMG [27; 28; 29; 30; 31; 32]. In [27; 28; 29; 30; 31; 32],  
Figure 4: The architecture of the state in the LSTM cell.  
since the average number of participants in the literature was aggregated as 6.5, we could confirm that our DB had a general size. The subjects were divided by two groups, and the groups did not overlap each other. In addition, one group was employed in order to train the model, and another group was used as the test DB. At this time, we evaluated the model performance through the cross-validation test of the two groups. The groups were divided as similarly as possible by body weight (group1: 70.0 \(\pm\) 5.9 kg, group2: 64.3 \(\pm\) 10.3 kg), age (group1: 33.7 \(\pm\) 11.0 years, group2: 28.3 \(\pm\) 2.6 years), and gender ratio (group1: 1 female and 2 male, group2: 1 female and 2 male).  
To evaluate the performance of the proposed algorithm, the two subjects were divided into two groups, and we used one of the two group as the training DB. The remaining DB was employed as the test DB that was not included in the model learning.