# An Interventional Surgical Robot Based on Multi-Data Detection
## 4 Data Training and Results  
### Long Short-Term Memory Network  
With the improvement of computer performance, machine learning algorithms are applied in various fields more frequently. Among them, the cyclic neural network (RNN) has been widely used. RNN networks are commonly used for tasks that target sequence data, such as image captioning, machine translation and classification prediction. The RNN network consists of circulating cells that allow information to be retained for subsequent  
Figure 8: (**a**) Catheter pushing in the vessel model; (**b**) data sequence of strain gauges and the force sensor.  
computation. However, it also has the problem of gradient explosion and gradient disappearance in the training process [23]. Long short term memory (LSTM) network based on RNN can solve these defects. LSTM network processes sequence information through a memory unit and gate mechanism, and has long-term dependence capability, which is superior to traditional RNN [24]. As shown in Figure 9, LSTM cells, in contrast to RNNS, consist of input gates, forget gates and output gates, with cell states to regulate and protect the flow of information within the cells. To achieve this, input gate ig and process input gate control the updated input values into the memory unit, while forgetting gate fg controls what information should be retained in the previous state of the unit [25]. Subsequently, the output gate og regulates what information received in the update cell state is output and dispatched to the hidden state.  
In this paper, we installed three groups of sensor devices at the slave side of the robot system. The three groups of sensors are the two groups of strain gauges in the catheter head and the force sensors in the catheter clamping module. They all output analog voltage signals, so we chose ADC inside the single chip microcomputer (STM32F030) to collect voltage and save data. The sampling rate is set at 2 Hz to ensure the synchronization of the data sequences of the three groups of sensors. The output sequence of the two groups of strain gauges in the catheter was set as \(\alpha\) and \(\beta\), and the output sequence of the force sensor in the clamping module was set as \(\gamma\). In this way, three sets of sequences can be combined into a three-dimensional vector \(A\), in which the three feature vectors are  
\[A=\begin{cases}\alpha\\ \beta\\ \gamma\end{cases} \tag{1}\]  
Figure 10 shows the structure of the LSTM predictive classification network used in this paper. Sequence group \(A\) has three feature dimensions, which are fed into the LSTM layer with 100 hidden units as the input of the network. The dropout layer sets the dropout probability to 0.5, which means that each neuron has a 50% probability of being removed, so that the training of one neuron does not depend on another neuron and the synergistic interaction between features is weakened to prevent overfitting of the network. Finally, through the full connect layer and the softmax layer, the results are converted into probabilities to realize the classification of data sequences.  
Figure 9: The LSTM unit structure diagram.  
Figure 10 shows the structure of the LSTM predictive classification network used by the surgical robot system. Sequence group A has three feature dimensions and is fed together as input into the LSTM layer with 100 hidden units. The dropout layer has a dropout probability of 0.5, which means that each neuron has a 50% chance of being removed, which weakens the synergy between features and prevents overfitting of the network output. Finally, through the full connection layer and softmax layer, the results are converted into probabilities to realize the classification of data series.