The hypothesis that the central nervous system (CNS) makes use of synergies or movement primitives in achieving simple to complex movements has inspired the investigation of different types of synergies. Kinematic and muscle synergies have been extensively studied in the literature, but only a few studies have compared and combined both types of synergies during the control and coordination of the human hand. In this paper, synergies were extracted first independently (called kinematic and muscle synergies) and then combined through data fusion (called musculoskeletal synergies) from 26 activities of daily living in 22 individuals using principal component analysis (PCA) and independent component analysis (ICA). By a weighted linear combination of musculoskeletal synergies, the recorded kinematics and the recorded muscle activities were reconstructed. The performances of musculoskeletal synergies in reconstructing the movements were compared to the synergies reported previously in the literature by us and others. The results indicate that the musculoskeletal synergies performed better than the synergies extracted without fusion. We attribute this improvement in performance to the musculoskeletal synergies that were generated on the basis of the cross-information between muscle and kinematic activities. Moreover, the synergies extracted using ICA performed better than the synergies extracted using PCA. These musculoskeletal synergies can possibly improve the capabilities of the current methodologies used to control high dimensional prosthetics and exoskeletons.
Investigations on how the central nervous system (CNS) effortlessly conducts complex hand movements have led to an extensive study of synergies or movement primitives. Of the different types of hand synergies, kinematic and muscle synergies have been widely studied in literature, but only a few studies have fused both. In this paper kinematic and muscle activities recorded from the activities of daily living were first fused and then dimensionally reduced through principal component analysis (PCA). By using these principal components or musculoskeletal synergies in a weighted linear combination, the recorded kinematics and muscle activities were reconstructed. The performance of these musculoskeletal synergies in reconstructing the movements was compared to the kinematic and muscle synergies reported previously in the literature by us and others. The results from these findings indicate that musculoskeletal synergies perform better than the synergies extracted without fusion. These newly demonstrated musculoskeletal synergies might improve neural control of robotics, prosthetics and exoskeletons.Clinical Relevance-In this paper, musculoskeletal synergies were extracted from the fusion of kinematic and muscle activities recorded from the activities of daily living. These newly demonstrated musculoskeletal synergies might enhance our understanding of neural control of robotics, prosthetics and exoskeletons.
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