## 3 Results and Discussion
### Performance Comparison  
The experimental results are shown in Figure 7. It shows that our proposed LS-TCN can achieve an accuracy (measured by correlation coefficient) of 71.6% for sEMG-based continuous motion estimation (six common gripping actions in Ninapro in this case). Compared with TCN [31], the accuracy of LS-TCN was improved by 6.6%. As shown in Figure 8, LS-TCN can achieve the best average RMSE performance. For subjects 4 and 6, it was not the best, because the convolution structure more easily produces jitter than the method with the recurrent structure. However, the convolution structure is easy to accelerate in hardware and thus can provide better real-time performance [35], which was demonstrated in our previous work [36]. Note that individual factors such as low muscle mass or obesity may lead to poor performance of the model. Figure 6: The atomic segment is defined as the shortest time sequence in which sEMG can express effective information. When the convolutional kernel is too small, it is difficult to obtain effective information. If the convolutional kernel is too large, it will contain redundant information, which increases the difficulty for the network to learn efficient information and increase the number of network parameters. Figure 7: Summary of the PCC of 8 subjects. PCC denotes the correlation between predicted joint angles and real joint angles. The higher, the better. Figures 9-11 display orange real joint angles from measurements and blue predicted joint angles estimated from sEMG signals using different methods. Although the blue predicted values look similar to sEMG signals, they are actually predicted joint angles. Two joints were used for each finger (as depicted in Figure 2), and therefore there are 10 subfigures (indexed from 1 to 10) for five fingers in total. Note that for each movement, there was a significant joint angle amplitude variation, which resulted in a peak or a valley. In each subfigure, two peaks or two valleys denote the same movement, because every movement was performed twice with the same duration in our test dataset. Thus there are 12 peaks/valleys for selected six grasping movements. Subfigure 1 of Figure 9 illustrates the division of six movements with two repetitions. Figure 8: Summary of the RMSE of 8 subjects. RMSE denotes the root mean square error between predicted joint angles and real joint angles. The lower, the better. Figure 9: The continuous amplitudes of real and predicted joint angles using RNN. There are 10 subfigures for 5 fingers (i.e., two joints per finger as depicted in Figure 2), and each subfigure denotes the result of a finger joint, where the x-axis represents the sampling point index and the y-axis denotes normalized joint angles. Each movement was performed twice, and thus every two peaks or valleys represents a movement; i.e., 6 grasping movements were characterized by 12 peaks/valleys in each subfigure. The annotations of subfigure 1 indicate the division of 6 movements with 2 repetitions. Figure 11: The continuous amplitudes of real and predicted joint angles using LS-TCN. There are 10 subfigures for 5 fingers (i.e., two joints per finger as depicted in Figure 2), and each subfigure denotes the result of a finger joint, where the x-axis represents the sampling point index and the y-axis denotes normalized joint angle. Each movement was performed twice, and thus two peaks or two valleys represents one repeated movement; i.e., 6 grasping movements are characterized by 12 peaks/valleys in each subfigure. Figure 10: The continuous amplitudes of real and predicted joint angles using TCN (kernel = 3). There are 10 subfigures for 5 fingers (i.e., two joints per finger as depicted in Figure 2), and each subfigure denotes the result of a finger joint, where the x-axis represents the sampling point index and the y-axis denotes normalized joint angles. Each movement was performed twice, and thus every two peaks or valleys represents a movement; i.e., 6 grasping movements were characterized by 12 peaks/valleys in each subfigure.