# Shoulder-Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration
## 2 COANN Network Optimization Algorithm Architecture
### COANN Network Optimization Algorithm
Taking the data of subject A3 as an example, the combination of multi-information and single sEMG as input data were predicted separately by the COANN, and the comparison of the prediction results is shown in Figure 11.

According to Figure 11, under the same algorithm conditions, when both the multi-information combination and the single sEMG information reached the wave peak, the curve of the multi-information combination was closer to the real value and had a smaller error; therefore, obviously the multi-information prediction accuracy is higher. The curve of the single sEMG information source had a larger error; therefore, obviously the single sEMG information source has a lower prediction accuracy. According to Tables 2 and 7, when utilizing the same COANN algorithm, the arithmetic mean values of the RMSE and R\({}^{2}\) of the shoulder and elbow joints obtained by comparing the multi-information and single sEMG are as follows: the arithmetic mean values of the shoulder and elbow joints' RMSE of the multi-information were higher than those of the single sEMG by 12.38% and 16.19%, respectively, and the arithmetic mean values of the R\({}^{2}\) were higher by 2.94% and 2.68%, respectively. ## 6 Summary

In this paper, for the problem of accuracy of upper-limb shoulder and elbow joint motion prediction, an artificial neural network (COANN) based on the Cheetah Optimization Algorithm (COA) is proposed to carry out the research of upper-limb shoulder and elbow joint motion angle prediction by taking stretching exercise as an example. The results show that:

(1) When combining multi-information sources for prediction using the COANN, RBF, and ANN, respectively, the prediction response time of the COANN is 22.6% shorter than that of the RBF and 35.1% shorter than that of ANN. Combining sEMG and shoulder and elbow joint angles as system inputs, using the RMSE and R\({}^{2}\) as evaluation indexes, and utilizing a multi-information combination approach, we compared 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: the arithmetic mean values of shoulder and elbow joints' RMSE of the COANN were 20.05% and 27.39% higher than those of the RBF, respectively, and the arithmetic mean values of R\({}^{2}\) were 9.95% and 12.61% higher, respectively. The arithmetic mean values of the RMSE of shoulder and elbow joints of the COANN were 34.33% and 34.01% higher than that of ANN, respectively, and the arithmetic mean values of R\({}^{2}\) were 11.97% and 18.61% higher, respectively. By incorporating the COANN, CNN, and LSTM, with sEMG (surface electromyography) and shoulder/elbow joint angles as input to the system, and through data estimation using the RMSE and R\({}^{2}\), the arithmetic means of the RMSE values for the shoulder and elbow joints of the COANN were 13.02% and 16.78% lower than those of the RBF, respectively, and the arithmetic means of the R\({}^{2}\) values were 4.52% and 3.16% higher, respectively. Compared with the ANN, the arithmetic means of the RMSE values for the shoulder and elbow joints of the COANN were 12.81% and 17.81% lower, respectively, and the arithmetic means of the R\({}^{2}\) values were 4.42% and 4.94% higher, respectively. (2) When utilizing the COANN algorithm with inputs of multi-information and single sEMG, respectively, the combination of multi-information improves the prediction accuracy of the elbow joint by 5.7% and the shoulder joint by 6.9% over the single sEMG. Using the RMSE and R\({}^{2}\) as evaluation indexes, the arithmetic mean values of the RMSE and R\({}^{2}\) for shoulder and elbow joints obtained from multi-information and single sEMG were compared when utilizing the same COANN algorithm: the arithmetic mean values of the RMSE for shoulder and elbow joints with multi-information were higher than that of single sEMG by 12.38% and 16.19%, respectively, and the arithmetic mean values of R\({}^{2}\) were higher than that of single sEMG by 2.94% and 2.68%, respectively.