# Deep-Learning-Based Models for Pain Recognition: A Systematic Review
## 3 Review Papers
### Multi-Model-Based Pain Recognition
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** &
\begin{tabular}{c} **Deep-Learning** \\ **Approaches** \\ \end{tabular} & **Task** & **Features-Devices** & **Dataset** & **Metric-Score** \\ \hline
2018 [11] & CNN and LSTM & \begin{tabular}{c} Feature extraction \\ and classification \\ \end{tabular} & \begin{tabular}{c} Face \\ -Microsoft Kinect Version2 \\ -Avis Q1922 thermal \\ camera \\ \end{tabular} & \
\begin{tabular}{c} Collected: Multimodal Intensity Pain \\ (MIIntPANI) database \\ Healthy subjects \\ Classes: 5 \\ \end{tabular} &
\begin{tabular}{c} 5-fold cross-validation \\ Accuracy \\ The confusion matrix \\ \end{tabular} \\ \hline
2017 [16] & MT-NN & Classification & \begin{tabular}{c} ECG, SC \\ Face \\ \end{tabular} & \
\begin{tabular}{c} Available: BioVid Heat Pain database \\ \end{tabular} &
\begin{tabular}{c} MAE, RMSE, ICC \\ \end{tabular} \\ \hline
2017 [17] & NN & \begin{tabular}{c} Confidence estimation \\ \end{tabular} & \begin{tabular}{c} ECG, SC, EMG \\ Face \\ \end{tabular} & \
\begin{tabular}{c} Available: BioVid Heat Pain database \\ \end{tabular} &
\begin{tabular}{c} Cross validation \\ RMSE: 0.347 \\ CC: 0.183 \\ \end{tabular} \\ \hline
2017 [13] & LSTM (Tensor Flow) & Classification &
\begin{tabular}{c} Tomography lumbar \\ spine pictures-norm Meta \\ Picture (MHD) average \\ \end{tabular} & Classes: 6 & 65\% \\ \hline
2018 [14] & LSTM & Classification &
\begin{tabular}{c} -Kinematic data \\ -Motion sensors \\ \end{tabular} & 22 healthy people and 22 LBP patients & Accuracy: 97.2\% \\ \hline
2019 [18] & CNNs & \begin{tabular}{c} Feature extraction \\ and classification \\ \end{tabular} & \
\begin{tabular}{c} EDA, ECG, EMG \\ (part1) \\ \end{tabular} &
\begin{tabular}{c} Available: BioVid Heat Pain database \\ Accuracy: 84.40\% \\ \end{tabular} \\ \hline
2019 [19] & LSTM & Classification & \begin{tabular}{c} Kinematic data \\ EMG \\ \end{tabular} & \
\begin{tabular}{c} Available: EmoPain database \\ \end{tabular} &
\begin{tabular}{c} mean F1:0.815 \\ \end{tabular} \\ \hline
2019 [15] & LSTM with attention & Classification & \begin{tabular}{c} Kinematic data \\ \end{tabular} & \
\begin{tabular}{c} Available: EmoPain database \\ \end{tabular} &
\begin{tabular}{c} mean F1: \\ 0.844 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._On the other hand C. [PERSON] et al. in 2019 [PERSON] et al. (2019) recognize specific behavior of chronic pain by deep learning. Their main objective is to determine the protective behavior of LBP patients during performing five exercises. They used sEMG and body movement data from available data set (emo-pain data set). This study proposed two recurrent neural networks which are called stacked LSTM and dual-LSTM. From five activities only, they computed the angles and energies of Mockup data. Regarding the muscle activity, they used the rectified sEMG data for smoothing and decreasing the noise of raw data. To update the weights, Adam optimizer was used with affixed learning rate of 0.001. Sliding window technique was used for data segmentation. In order to analyze and select the best length of window, they performed different experiments for each activity to determine the best length of window using a fixed overlapping ratio of 75%. As a result, they found the 3 s is the best length of window that met best detection for the most activities. In this study, Jittering and Random discarding are the augmentation methods that were used, providing a better performance than using the original dataset. They also found the combination of the two augmentation methods provide the best performance. Their final results obtained the best performance of 0.815 mean F1 score for their LSTM Networks that was better than the conventional neural network. However, the generalization will lead to a decrease in performance which needs to be fixed and solved. The main limitation of this study is the important equally when determine the protective behavior during all body parts, activity types and time.