## 3 Results  
For the traditional MSM, we used the channel selection method suggested by [PERSON] et al. (2015). The two criteria are as follows: (1) the channel must be in the area where the expected active muscle is located, and (2) the highest correlation exists between the ICs and the acquired measured joint motion. For the Synergy-MSM and regression methods, the CC was calculated between each IC component based on the angle measured during finger flexion and extension. To determine the optimal number of ICs, two ICs were initially selected, and the IC component with the highest CC was gradually selected as the input data. The ICs with the highest CC for flexion and extension of the joint were prioritized for selection, and the synergy was calculated using NMF, followed by the modeling and evaluation of the estimated performance of the model. This stepwise process continued until 16 channels (half the number of ICs related to flexion and extension) were selected. The average NRMSE of the five joints was then calculated from the angles predicted by the models and the measured angles to evaluate the predictive performance of each model with different numbers of ICs and determine the best model. Figure 5 shows the prediction performance of each model for different numbers of ICs for Subject 5. The results show that for Subject 5, the average NRMSE was lowest when 12 ICs were selected for modeling in the Synergy-MSM; for the Synergy-LRM, the average NRMSE was lowest when 4 ICs were selected for modeling; and for Synergy-SVR, the average NRMSE was lowest when 4 ICs were selected for modeling. Figure 5: Average NRMSE for prediction using different numbers of IC components for Subject 5. The IC components were selected from the angles measured during joint flexion and extension and the CC values between the IC components. Half the number of IC components are correlated with flexion and the other half are correlated with extension. Finally, the estimates from the four models were compared with the joint angles measured using motion capture to calculate CC and NRMSE. Statistical \(p\)-value below 0.05 was considered statistically significant. Figure 6 shows the prediction results for Subject 5 when the fitting was performed using the model with the optimal number of ICs for each method. The blue dashed lines represent the measured angles, and the red, yellow, purple, and green lines represent the data from the Synergy-MSM, MSM, Synergy-LRM, and Synergy-SVR, respectively. The results show that the proposed method can predict the MCP joints of finger and wrist movements. Moreover, the error for the developed model was lower than those of the other methods. The results for the thumb are shown in Figure 6. Figure 6c shows that the proposed method provides a better fit for predicting thumb extension motions (from the 0\({}^{\circ}\) to \(-^{\circ}\) direction), which were not predicted by other methods. Figure 7 shows the mean results for each joint for all the subjects, with the CC and NRMSE values expressed as boxplots and barplots, respectively. Table 1 presents the exact statistics for the four models. For the Synergy-MSM, average CC = 92.0 \(\pm\) 5% (Synergy-MSM vs. other models, \(p<\) 0.01); average NRMSE = 10.7 \(\pm\) 1% (Synergy-MSM vs. MSM and SVR, \(p<\) 0.01, Synergy-MSM vs. LRM, \(p<\) 0.05). For the MSM, the average CC was 88.0 \(\pm\) 9% (MSM vs. Synergy-MSM and SVR, \(p<\) 0.01), and the average NRMSE was 15.9 \(\pm\) 2% (MSM vs. other models, \(p<\) 0.01). For LRM, average CC = 87.9 \(\pm\) 9% (LRM vs. Synergy-MSM and SVR, \(p<\) 0.01), average NRMSE = 11.5 \(\pm\) 1% (LRM vs. Synergy-MSM and MSM, \(p<\) 0.01). For SVR, average CC = 89.9 \(\pm\) 9% (SVR vs. other models, \(p<\) 0.01), average NRMSE = 11.9 \(\pm\) 2% (SVR vs. Synergy-MSM and MSM, \(p<\) 0.01). This comparison demonstrates that the three models employing ICA-synergy to estimate the joint angles have lower CC and NRMSE values than those of the MSM, and the proposed Synergy-MSM shows better performance and robustness than those of the other models.