# Shoulder-Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration
## 2 COANN Network Optimization Algorithm Architecture
### COANN Network Optimization Algorithm

Table 4: Evaluation metrics for predicting upper extremity joint recognition angle signals by multi-information combination and ANN. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Elbow Joint**} & \multicolumn{2}{c}{**Shoulder Joint**} \\ \cline{2-5}  & **RMSE** & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** \\ \hline A1 & 0.003981 & 0.9959 & 0.008261 & 0.9978 \\ A2 & 0.004628 & 0.9896 & 0.012082 & 0.9895 \\ A3 & 0.003701 & 0.9998 & 0.004326 & 0.9921 \\ A4 & 0.004834 & 0.9928 & 0.009326 & 0.9822 \\ A5 & 0.006532 & 0.9993 & 0.003591 & 0.9899 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation metrics for predicting upper extremity joint recognition angle signals by multi-information combination and COANN. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Elbow Joint**} & \multicolumn{2}{c}{**Shoulder Joint**} \\ \cline{2-5}  & **RMSE** & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** \\ \hline A1 & 0.017071 & 0.9169 & 0.019825 & 0.9892 \\ A2 & 0.012738 & 0.9092 & 0.042262 & 0.9075 \\ A3 & 0.099933 & 0.9312 & 0.080688 & 0.9121 \\ A4 & 0.053222 & 0.9158 & 0.057479 & 0.9531 \\ A5 & 0.054987 According to Tables 2, 5 and 6, it can be seen that the arithmetic mean values of the RMSE and R\({}^{2}\) of shoulder and elbow joints obtained by three algorithms--the COANN, CNN, and LSTM--were compared using the multi-information combination approach: the arithmetic mean values of shoulder and elbow joints' RMSE of the COANN were higher than those of the CNN by 13.02% and 16.78%, respectively, and the arithmetic mean values of R\({}^{2}\) were higher than those of the CNN by 4.52% and 3.16%, respectively. The arithmetic mean values of shoulder and elbow joints' RMSE of the COANN was 12.81% and 17.81% higher than that of LSTM, and the arithmetic mean values of R\({}^{2}\) were 4.42% and 4.94% higher, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Elbow Joint**} & \multicolumn{2}{c}{**Shoulder Joint**} \\ \cline{2-5}  & **RMSE** & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** \\ \hline A1 & 0.093281 & 0.8937 & 0.130981 & 0.8996 \\ A2 & 0.117474 & 0.8818 & 0.190503 & 0.9108 \\ A3 & 0.018423 & 0.8764 & 0.109861 & 0.9213 \\ A4 & 0.101305 & 0.9177 & 0.123609 & 0.8956 \\ A5 & 0.087391 & 0.9116 & 0.072856 & 0.8981 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation metrics for predicting upper extremity joint recognition angle signals by multi-information combination and LSTM. Figure 10: Comparison of upper extremity joint angle prediction results based on COANN, CNN, and LSTM under multi-information. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Elbow Joint**} & \multicolumn{2}{c}{**Shoulder Joint**} \\ \cline{2-5}  & **RMSE** & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** \\ \hline A1 & 0.017071 & 0.9169 & 0.019825 & 0.9892 \\ A2 & 0.012738 & 0.9092 & 0.042262 & 0.9075 \\ A3 & 0.099933 & 0.9312 & 0.080688 & 0.9121 \\ A4 & 0.053222 & 0.9158 & 0.057479 & 0.9531 \\ A5 & 0.054987 & 0.9736 & 0.097954 & 0.9131 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics for predicting upper extremity joint recognition angle signals by multi-information combination and CNN. Comparison of Recognition Effect of Multi-Information Sources and Single Information Sources Under COANN Algorithm

In order to verify the influence of multi-information on the effect of system prediction accuracy, the shoulder and elbow joint motion angle prediction results were obtained for comparison based on the COANN using multi-modal information and single sEMG as inputs, respectively.