## 4 Discussion  
An MSM for predicting joint angles based on muscle synergy was developed, and its performance was evaluated by comparing it with the original method and other traditional prediction methods. According to our previous study, the proposed method uses ICA-based muscle synergy as the input for the model [46]. Specifically, the raw EMG signal was first decomposed using ICA, after which the independent components were extracted using NMF for muscle synergy. Because different numbers of independent components affect muscle synergy, the optimal number of extracted independent components must be confirmed. After confirming the optimal number of decomposed independent components for each subject, the extracted muscle synergy was used to train the Synergy-MSM. The results obtained with the Synergy-MSM were also compared with those of traditional MSM, LMR, and SVR. The reliability of the continuous estimation of joint angles using a muscle-synergy-driven model has not been investigated previously. Previous studies have demonstrated that the robustness and performance of motion classification are better when synergistic features are used than EMG feature sets [46,47]. [PERSON] compared the results with and without the MSM, and the current results of the MSM are compatible with the previous results [45]. Herein, it was confirmed that the MSM with muscle synergy is more effective for estimating continuous joint motion than the traditional MSM and also has excellent performance compared with other regression models. The results without the MSM had lower accuracy than that of the results with the MSM. In conclusion, the results using synergy activities were better than the results using each muscle activity with or without the MSM. Previous studies have investigated the optimal number and location of electrodes on the forearm to obtain information representative of muscle activation and motor robustness [48,49]. It has been pointed out that the user's arm position affects changes in the activity of the EMG signals, and different patterns of forearm muscle activity are observed during the same motion [49]. From this perspective, the performance of EMG-based motion prediction can be improved if specific locations of muscle activity can be identified during wrist and single-finger motion. In this study, attempts were made to identify specific muscle activity locations associated with motion using a topological plot of the weights of the independent components. The ICs of these muscle locations that contribute significantly to specific motions were then gradually increased from 2 to 16 in multiples of 2 to analyze how the 2 synergies computed from these ICs affect the predictive power of the model. Figure 7: Comparisons of five-fold cross-validation results of all models: (**a**) Average correlative coefficient of the best performance models of all subjects. (**b**) Average NRMSE of the best performance models of all subjects. The selected muscle activity locations were ranked from highest to lowest based on the IC component, by considering the CC of the joint angles during flexion and extension. Herein, the motor activation pattern of the index finger was selected for illustration. The topological plot for Subject 7 is shown in Figure 8a. Figure 8a shows the weight of the IC from the sensor locations, which are divided into two parts representing the flexor side (left) of flexion and extensor side (right) of the extension muscle groups, where the colors indicate the weights assigned to the specific ICs of each sensor in the EMG sensor. The synergy weight patterns and synergy vectors extracted through the NMF are shown in Figure 8b. Figure 8c shows the measured and estimated joint angles. The topological plot and muscle synergy data for the other subjects are provided in Supplementary Materials. In the flexion motion of the index finger, the muscle synergy representing flexion consists mainly of IC data around locations 1, 2, 10, 18, and 26 in the topological plot.