## 3 Results
### Subjects and Experimental Setup
#### 3.2.2 Force Estimation  
In order to optimize the network, accelerate the training speed, and improve the accuracy of prediction results, in this experiment, we used the set-aside method to divide the training set and the test set, and increase the proportion of the training set. As a result, we divided the datasets into a training part and a test part with a ratio of 8:2 to verify the performance of the model. To get the best performance of the model, the parameters of the models were fully tuned. Table 4 lists the hyper-parameters of our model, which show the layers, activation function, optimizer, dropout, initial learning rate, batch size, and epoch.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyper-Parameters** & **ResNet** & **BiLSTM** \\ \hline Layers & 101 & 128–128 \\ Activation Function & ReLU & ReLU \\ Optimizer & Momentum & Momentum \\ Dropout & 0.5 & 0.5 \\ Initial Lr & 0.0001 & 0.0001 \\ Batch Size & 128 & 128 \\ Epoch & 1000 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyper–parameters of our model for the dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Angle** & **Time** & **1 s** & **2 s** & **3 s** \\ \hline
30\({}^{\circ}\) & 100\% (MVC) & 100\% (MVC) & 100\% (MVC) \\
60\({}^{\circ}\) & 100\% (MVC) & 100\% (MVC) & 100\% (MVC) \\
90\({}^{\circ}\) & 100\% (MVC) & 100\% (MVC) & 100\% (MVC) \\
120\({}^{\circ}\) & 100\% (MVC) & 100\% (MVC) & 100\% (MVC) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiment procedures for each subject.  
Figure 9: Filter effect on the sEMG raw data.  
We compared the experimental results under three contraction times (1, 2, and 3 s), as shown in Figure 10. The subfigure (a-c) shows the raw sEMG signal. The subfigure (d-f) shows the EMG signal processed by the decoupling algorithm. The subfigure (g-i) shows the comparison between the actual measured interaction force and the predicted force. The subfigure (j-l) shows the mean absolute error of the actual measured interaction force and the predicted force.  
In order to comprehensively evaluate the effectiveness of the algorithm, NRMSE and coefficient of determination are taken into account to evaluate the performance of the model [39; 40], which are shown as below. The different window size, joint angle, and other state of the art methods were compared, and the comparison results are shown in Figures 11 and 12.  
Figure 11: Box diagram of evaluation of window size in the algorithm: (**a**) NRMSE; (**b**) Coefficient of Determination.  
Figure 10: Above are experimental results under three contraction conditions (1, 2, 3 s); (**a–c**) are the raw signal of sEMG; (**d–f**) are filtered signals after the decoupling algorithm; (**g–i**) are the comparisons of actual force and predicted force; (**j–l**) are the mean absolute error of the actual force and the predicted force.  
Based on the results shown in Figures 11 and 12, it can be concluded that: (1) For a given subject, our algorithm can obtain accurate estimation results for three different contraction ways. (2) It is very important to select the appropriate window size; too small a window results in the information between adjacent data not being fully mined, whereas too large a window will reduce the effect of estimation.  
Based on the results shown in Figure 12, it can be concluded that: (1) In different subjects, our algorithm achieved the best performance in coefficient of determination and NRMSE. LSTM is inferior to our algorithm and SVR is the worst. (2) Under the angle of 30, each algorithm has achieved good results. The reason is that the muscle would contract to the maximum at the fastest rate. The sEMG signal is less affected by non-stationary factors which are good for estimation performance.