# Shoulder-Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration
## 2 COANN Network Optimization Algorithm Architecture
### COANN Network Optimization Algorithm
Artificial Neural Network (ANN): The Artificial Neural Network (ANN) imitates biological neural structure and realizes nonlinear mapping through neuron connection and activation function (e.g., ReLU/Sigmoid). Because ANN serves as the original framework for the COANN, it can visually compare the prediction effect of the COANN. Radial Basis Function (RBF) Network: This is a three-layer feed-forward neural network that realizes nonlinear mapping through radial basis functions (e.g., Gaussian function) in the hidden layer, which is good at solving the function approximation nonlinear problem and has a fast training speed and a simple structure. In terms of regression prediction and as the most representative algorithm, the RBF is chosen to compare the prediction effect of the COANN. Convolutional Neural Network (CNN): The CNN lends local awareness and weight sharing to efficiently extract spatial level features and reduce the number of parameters; pooling enhances robustness, and end-to-end learning adapts to high-dimensional data. Long Short-Term Memory (LSTM): LSTM solves gradient vanishing by a gating mechanism, models long-distance dependency, dynamically integrates context, and is good at dealing with temporal sequences. Taking the subject A3 data as an example, the multi-information combination model is trained and predicted by the COANN, ANN, and RBF, and the training effect and comparison are as follows [30; 31; 32; 33; 34; 35; 36]. According to Figure 9, the COANN takes the shortest time when the COANN, RBF, and ANN all reach the wave peak in 0.48, 0.62, and 0.74 s, respectively, for the same input. The experimental results show that the COANN obtained after COA optimization has the shortest system runtime. In this paper, the RMSE and R\({}^{2}\) are used as evaluation indexes to analyze the multi-information combination and the COANN in order to predict the upper-limb joint recognition angle signal. The more the RMSE value tends to 0 and the more the R\({}^{2}\) value tends to 1 is proof that the prediction method of the COANN is better. According to Tables 2-4, it can be seen that the arithmetic mean values of the RMSE and R\({}^{2}\) of shoulder and elbow joints obtained by the three algorithms--the COANN, RBF, and ANN--were compared using the multi-information combination approach: the

Figure 1:8 Comparison of upper extremity joint angle prediction results based on COANN, RBF, and ANN under multi-information. arithmetic mean values of shoulder and elbow joints' RMSE of the COANN were higher than those of the RBF by 20.05% and 27.39%, respectively, and the arithmetic mean values of R\({}^{2}\) were higher than those of the RBF by 9.95% and 12.61%, respectively. The arithmetic mean values of shoulder and elbow joints' RMSE of the COANN were 34.33% and 34.01% higher than those of the ANN, and the arithmetic mean values for R\({}^{2}\) were 11.97% and 18.61% higher, respectively. Taking the subject A3 data as an example, the multi-information combination model is trained and predicted by the COANN, CNN, and LSTM, and the training effect and comparison are as follows. According to Figure 10, under the condition of the same input, when the COANN, CNN, and LSTM all reached the crest, the curve of the COANN combined with multi-source information was closer to the real value and had a smaller error. The experimental results show that the prediction accuracy of the COANN obtained after COA optimization was high. \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}