## 2 Materials and Methods
### Our Proposed Framework
#### 2.3.2 Force Estimation  
A Recurrent Neural Network (RNN) is a computational model designed to deal with the temporal features of the input signal. It is quite similar to a feed-forward neural network, and it can cycle in the network. The network can realize memory through the cycle, allowing it to combine the current input with the past several time steps. LSTM network is a branch of RNN that can remember the previous information and keep the error at a constant level [30]. It can let the recursive network establish a long distance connection. LSTM is quite suitable for processing time sequence data. It can exploit long-range dependencies in time series data and can furthermore lead to more  
Figure 5: Schematic diagram of residual block. efficient force estimation. Figure 6 shows the unit composition of the LSTM block which consists of the input gate, forget gate, and the output gate. The calculation process of LSTM can be summarized as follows: Calculate the useful information for the next moment by forgetting the information in the current cell state and memorizing new information from the new cell. The useless information needs to be discarded, and a hidden state will be output at each time step. Forget Gate: The information to be saved is determined by the current input, the state at the previous moment, and the output of the previous moment. \[f_{t}=\sigma\left(W_{t}\cdot[h_{t-1},x_{t}]+b_{f}\right) \tag{7}\]  
where \(h_{t-1}\) is hidden state of the previous moment, \(x_{t}\) is the current input, \(W_{i}\) is the weight parameter, \(b_{f}\) is bias, and \(f_{t}\) is the output of the forget gate. Input Gate: Memorize the information you need to remember. \[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{8}\]  
\[\widetilde{C}=\tanh\left(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}\right) \tag{9}\]  
where \(h_{t-1}\) is the hidden state of the previously hidden layer, \(x_{t}\) is the current input, \(b_{i},\ b_{C}\) is bias term, \(i_{t}\) is the output of the input gate, and \(\widetilde{C}\) is the candidate information of the unit memory at the current moment. Status updates: Calculates the current cell state at the moment. \[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{10}\]  
where \(i_{t}\) is the output of the input gate, \(f_{t}\) is the output of forget gate, \(C_{t-1}\) is the cellular state of the previous moment, \(\widetilde{C}_{t}\) is the temporary cellular state. Output Gate: The information to be output is determined based on the latest state, the output of the previous moment, and the current state. \[o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{11}\]  
\[h_{t}=o_{t}*\tanh\left(C_{t}\right) \tag{12}\]  
where, \(o_{t}\) is the output of the output gate, \(h_{t-1}\) is the hidden state of the previously hidden layer, \(x_{t}\) is current input, \(b_{o}\) is bias term, \(h_{t}\) is the hidden state, \(C_{t}\) is the cellular state of the current moment. Figure 6: Unit composition of the LSTM block. However, there were two common problems with the model using LSTM: the vanishing gradient problem and the inability to encode information back to the front. BiLSTM networks mitigate this problem by combining two normal LSTMs, information can flow both in the forward and backward time direction. The structure of BiLSTM consists of a forward LSTM cell and a backward LSTM cell. Figure 7 shows a schematic of a BiLSTM. Where the \(X_{1},X_{2},\ldots,X_{T}\) represent the outputs of the previous layer. sEMG features series contain different temporal information, and not all features contribute equally to the estimate of force. By introducing an attention mechanism, encoding the full input sequences into a fixed-length vector is no longer needed. A neural network with attention mechanism has recently shown success in a wide range of tasks, such as machine translation, time series prediction, and speech recognition [31; 32; 33]. In this part, we employed an attention mechanism to improve the learning efficiency and ability to learn important features for regression tasks.