## 4 Discussion
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{5}{c}{**(a)**} \\ \hline
**Method** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** & **\(p\)-Value** & \
\begin{tabular}{l} **Training** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{l} **Forecast** \\ **Time (ms)** \\ \end{tabular} \\ \hline SMA-BLS & \(0.0019\pm 0.0001\) & \(0.0366\pm 0.0012\) & \(0.0098\pm 0.0006\) & \(0.970\pm 0.002\) & / & \(174.3\pm 5.36\) & \(4.1\pm 0.32\) \\ CNN & \(0.0041\pm 0.0003\) & \(0.0689\pm 0.0015\) & \(0.0694\pm 0.0026\) & \(0.932\pm 0.002\) & \(0.00760\) & \(259.2\pm 13.67\) & \(21.4\pm 2.41\) \\ SVM & \(0.0044\pm 0.0002\) & \(0.0728\pm 0.0013\) & \(0.0762\pm 0.0027\) & \(0.926\pm 0.003\) & \(0.00519\) & \(336.6\pm 11.94\) & \(19.8\pm 2.63\) \\ BP & \(0.0053\pm 0.0003\) & \(0.0788\pm 0.0025\) & \(0.0869\pm 0.0048\) & \(0.919\pm 0.004\) & \(0.00218\) & \(95.6\pm 2.67\) & \(11.6\pm 1.80\) \\ ELM & \(0.0048\pm 0.0003\) & \(0.0668\pm 0.0011\) & \(0.0709\pm 0.0016\) & \(0.923\pm 0.002\) & \(0.00308\) & \(135.5\pm 5.19\) & \(16.3\pm 2.24\) \\ RF & \(0.0058\pm 0.0003\) & \(0.0887\pm 0.0024\) & \(0.0908\pm 0.0034\) & \(0.909\pm 0.002\) & \(0.00143\) & \(168.3\pm 6.95\) & \(26.5\pm 1.31\) \\ RBF & \(0.0042\pm 0.0003\) & \(0.0619\pm 0.0026\) & \(0.0669\pm 0.0031\) & \(0.916\pm 0.002\) & \(0.00394\) & \(195.3\pm 6.74\) & \(13.9\pm 1.08\) \\ LSTM & \(0.0039\pm 0.0002\) & \(0.0597\pm 0.0011\) & \(0.0686\pm 0.0014\) & \(0.936\pm 0.003\) & \(0.00821\) & \(267.4\pm 8.18\) & \(14.2\pm 1.94\) \\ \hline \multicolumn{1}{c}{**(b)**} \\ \hline
**Method** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** & **\(p\)-Value** & \
\begin{tabular}{l} **Training** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{l} **Forecast** \\ **Time (ms)** \\ \end{tabular} \\ \hline SMA-BLS & \(0.0020\pm 0.0001\) & \(0.0387\pm 0.0009\) & \(0.0108\pm 0.0007\) & \(0.963\pm 0.001\) & / & \(176.6\pm 5.44\) & \(4.2\pm 0.27\) \\ CNN & \(0.0043\pm 0.0002\) & \(0.0679\pm 0.0014\) & \(0.0699\pm 0.0016\) & \(0.931\pm 0.001\) & \(0.00760\) & \(257.8\pm 13.16\) & \(20.1\pm 2.32\) \\ SVM & \(0.0041\pm 0.0003\) & \(0.0736\pm 0.0012\) & \(0.0746\pm 0.0023\) & \(0.925\pm 0.004\) & \(0.00497\) & \(337.7\pm 10.58\) & \(19.2\pm 2.56\) \\ BP & \(0.0056\pm 0.0003\) & \(0.0759\pm 0.0027\) & \(0.0832\pm 0.0035\) & \(0.918\pm 0.003\) & \(0.00196\) & \(98.3\pm 2.57\) & \(10.9\pm 1.73\) \\ ELM & \(0.0045\pm 0.0004\) & \(0.0691\pm 0.0013\) & \(0.0716\pm 0.0021\) & \(0.924\pm 0.002\) & \(0.00321\) & \(138.2\pm 5.11\) & \(16.7\pm 2.15\) \\ RF & \(0.0057\pm 0.0003\) & \(0.0896\pm 0.0026\) & \(0.0942\pm 0.0025\) & \(0.910\pm 0.003\) & \(0.00137\) & \(169.6\pm 6.82\) & \(26.3\pm 1.24\) \\ RBF & \(0.0045\pm 0.0004\) & \(0.0608\pm 0.0021\) & \(0.0618\pm 0.0029\) & \(0.912\pm 0.003\) & \(0.00364\) & \(194.2\pm 6.63\) & \(13.8\pm 1.14\) \\ LSTM & \(0.0038\pm 0.0002\) & \(0.0588\pm 0.0012\) & \(0.0662\pm 0.0013\) & \(0.937\pm 0.002\) & \(0.00842\) & \(262.6\pm 8.08\) & \(14.3\pm 1.84\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: (a) Prediction results of different algorithms during arm side raises. (b) Prediction results of different algorithms during arm front raises. (c) Prediction results of different algorithms during elbow curls. the BLS by leveraging its foraging behavior, adaptability, and self-organization properties, resulting in a high-precision, computationally efficient joint angle prediction method. From the results, we can find that as time in the future increased, the training accuracy and real-time prediction accuracy of the joint prediction model decreased. In addition, with the increase in the weight of the hand load, the training accuracy and real-time prediction accuracy of the joint prediction model increased. This may be because the load causes the amplitude of the MMG signal to increase, which leads to an increase in the proportion of effective information in the MMG signal, and ultimately leads to an improvement in prediction accuracy.