###### Abstract  
Recently, many muscle synergy-based human motion prediction models and algorithms have been proposed. In this study, the muscle synergies extracted from electromyography (EMG) data were used to construct a musculoskeletal model (MSM) to predict the joint angles of the wrist, thumb, index finger, and middle finger. EMG signals were analyzed using independent component analysis to reduce signal noise and task-irrelevant artifacts. The weights of each independent component (IC) were converted into a heat map related to the motion pattern and compared with human anatomy to find a different number of ICs matching the motion pattern. Based on the properties of the MSM, non-negative matrix factorization was used to extract muscle synergies from selected ICs that represent the extensor and flexor muscle groups. The effects of these choices on the prediction accuracy was also evaluated. The performance of the model was evaluated using the correlation coefficient (CC) and normalized root-mean-square error (NRMSE). The proposed method has a higher prediction accuracy than those of traditional methods, with an average CC of 92.0% and an average NRMSE of 10.7%. EMG; ICA; muscle synergy; musculoskeletal model +
Footnote †: journal: Applied  
_Applied_  
Footnote †: journal: Applied
assessments); the correlations between the predicted and measured forces were 0.840, 0.770, and 0.789, respectively. Machine learning-based prediction methods tend to use feature data to establish a relationship between EMG signals and forearm motion. However, these models cannot explain the EMG signals and neural instructions. To develop models in which the internal parameters are directly correlated with muscle tension, some researchers have attempted to build mathematical muscle models using EMG-based signals [13; 14; 15]. [PERSON] et al. developed a musculoskeletal model (MSM) based on anatomical and physiological data to estimate joint torque using EMG [16]. [PERSON] et al. further improved the MSM and used it to simultaneously predict the angle and stiffness of the wrist [17]. These models require the use of muscle-specific EMG signals to adjust the parameters and adapt the model to the individual to obtain the best predictive performance. Thus, a key challenge to this approach is the lack of EMG data for muscles that contribute significantly to the joint torque. This is because surface electrodes cannot measure EMG signals from deeper muscles, and several issues need to be addressed, including crosstalk noise between the adjacent muscles. The issue of crosstalk noise in EMG signals is more severe when using high-density (HD) surface electromyogram grids or array EMG systems than when using conventional EMG electrodes [18]. To separate specific deep muscle signal sources, a blind source separation algorithm called independent component analysis (ICA) was used to separate overlapping muscle signals in the sensor. The advantages of ICA have been confirmed by several studies on EMG to obtain deep muscle source signals from performed motions, such as reducing errors in gesture classification [19] and improving the accuracy of force prediction [20]. In addition to the aforementioned advantages, another advantage of ICA is the compression of the incoming force signal, which reduces the computational cost. The human musculoskeletal system has a highly redundant structure, and the redundancy issue remains an unresolved problem. To solve this redundancy problem, an approach called muscle synergy has been proposed, in which the motor system is reduced to a module in which multiple muscles work in a coordinated manner [21; 22; 23]. Muscle synergy was extracted from the EMG signal, where each synergy consisted of two components: the weight of the muscle under synergy, and its time-varying synergy excitations. Muscle synergy has been widely used in the study of human limb movements to analyze the muscle activation patterns in motor tasks [24; 25; 26]. Several recent studies have used muscle synergy information for predictive analyses [27; 28].