Muscle Synergies Extracted Using Principal Activations: Improvement of Robustness and Interpretability

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 2; pp. 453 - 460
• Main Authors: Ghislieri, Marco, Agostini, Valentina, Knaflitz, Marco
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Aged; Algorithms; Biomechanical Phenomena; Biomechanics; Central nervous system; Clustering algorithms; Cutoff frequency; Electromyography; EMG; Female; Gait - physiology; gait analysis; Humans; interpretability; Locomotion; Locomotion - physiology; Male; Middle Aged; muscle activations; muscle synergies; Muscle, Skeletal - physiology; Muscles; principal activations; Prototypes; Reproducibility of Results; Robustness; Signal processing; Task analysis; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2020.2965179

The muscle synergy theory has been widely used to assess the modular organization of the central nervous system (CNS) during human locomotion. The pre-processing approach applied to the surface electromyographic (sEMG) signals influences the extraction of muscle synergies. The aim of this contribution is to assess the improvements in muscle synergy extraction obtained by using an innovative pre-processing approach. We evaluate the improvement in terms of the possible variation in the number of muscle synergies, of the intra-subject consistency, of the robustness, and of the interpretability of the results. The pre-processing approach presented in this paper is based on the extraction of the muscle principal activations (muscle activations strictly necessary to accomplish a specific biomechanical task) from the original sEMG signals, to then obtain muscle synergies using principal activations only. The results herein presented show that the application of this novel approach for the extraction of the muscle synergies provides a more robust and easily interpretable description of the modular organization of the CNS with respect to the standard pre-processing approach.