# 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
2017 & Multitask neural network & Classification & \
\begin{tabular}{c} Skin conductance (SC) \\ and heart-rate features \\ (ECC) only \\ \end{tabular} & Available: BioVid Heat Pain database &
\begin{tabular}{c} Accuracy \\ 82.7\% \\ \end{tabular} \\ \hline
2017 & Long-short term memory & \begin{tabular}{c} Features extraction \\ neural networks (LSTMs) \\ \end{tabular} & \begin{tabular}{c} Vocal from audio \\ Feature extraction \\ \end{tabular} & \begin{tabular}{c} Colected: Triage Pain-Level Multimodal \\ database \\ \end{tabular} & \
\begin{tabular}{c} WAR \\ Available: Speech data: Chinese corpus: \\ The DAIA database \\ \end{tabular} &
\begin{tabular}{c} 72.3\%: binary classes \\ 54.2\%: three-class classes \\ \end{tabular} \\ \hline
2017 & LSTMs & Classification & Face & \
\begin{tabular}{c} Available: UNBC-MacMaster-Shouder \\ Pain Expression Archive database \\ \end{tabular} &
\begin{tabular}{c} MAE 2.47 (0.18) \\ ICC 0.36 (0.08) \\ Confusion matrices \\ \end{tabular} \\ \hline
2018 & \begin{tabular}{c} -Convolutional neural \\ networks (CNNs) \\ -1-STMs \\ \end{tabular} & \begin{tabular}{c} -Feature extraction \\ -Classification \\ \end{tabular} & Face & \
\begin{tabular}{c} Available: UNBC-MacMaster-Shouder \\ Pain Expression Archive database \\ \end{tabular} &
\begin{tabular}{c} MAE 2.47 (0.18) \\ ICC 0.36 (0.08) \\ Confusion matrices \\ \end{tabular} \\ \hline
2018 & \begin{tabular}{c} -Convolutional neural \\ networks (CNNs) \\ -1-STMs \\ \end{tabular} & \begin{tabular}{c} -Feature extraction \\ -Classification \\ \end{tabular} & Face & \
\begin{tabular}{c} Available: UNBC-MacMaster-Shouder \\ Pain Expression Archive database \\ \end{tabular} &
\begin{tabular}{c} AUC: 93.3\% \\ \end{tabular} \\ \hline
2017 & CNN & Feature extraction & Face & \
\begin{tabular}{c} Available: UNBC-MacMaster-Shouder \\ Pain Expression Archive database \\ \end{tabular} &
\begin{tabular}{c} CORR 0.67 \\ RMSE 0.99 \\ \end{tabular} \\ \hline
2017 & CNN-Fine-tuning- & Classification & Face & \
\begin{tabular}{c} Available: UNBC-MacMaster-Shouder \\ Pain Expression Archive database \\ \end{tabular} &
\begin{tabular}{c} Unweighted Metrics \\ MAE 0.389 \\ MSE 0.804 \\ PCC 0.651 \\ Weighted Metrics: \\ Weighted MAE 0.991 \\ \end{tabular} \\ \hline
2018 & Cumulative attributes & \begin{tabular}{c} Classification \\ \end{tabular} & Face & \
\begin{tabular}{c} Available: UNBC-MacMaster-Shouder \\ Pain Expression Archive database \\ \end{tabular} &
\begin{tabular}{c} Regression: \\ RMSE (1.20, 1.29) \\ MCC 0.36, 0.41 \\ RMSE (1.17, 1.19) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of pain-recognition studies based on deep learning. Abbreviations: WAR—weighted average recall; AUC—area under the curve; CORR—Pearson’s correlation; MSE—mean-square error; RMSE—root mean-square error; ICC—intraclass correlation; CC—correlation coefficient; MAE—mean absolute error.