Muscle synergies as a predictive framework for the EMG patterns of new hand postures

• Published in: Journal of neural engineering Vol. 6; no. 3; p. 036004
• Main Authors: Ajiboye, A B, Weir, R F
• Format: Journal Article
• Language: English
• Published: England 01-06-2009
• Subjects: Adult; Algorithms; Computer Simulation; Electromyography - methods; Female; Hand - physiology; Humans; Male; Models, Neurological; Motor Skills - physiology; Postural Balance - physiology; Posture - physiology; Sign Language; Task Performance and Analysis
• ISSN: 1741-2552
• DOI: 10.1088/1741-2560/6/3/036004

Synchronous muscle synergies have been suggested as a framework for dimensionality reduction in muscle coordination. Many studies have shown that synergies form a descriptive framework for a wide variety of tasks. We examined if a muscle synergy framework could accurately predict the EMG patterns associated with untrained static hand postures, in essence, if they formed a predictive framework. Hand and forearm muscle activities were recorded while subjects statically mimed 33 postures of the American Sign Language alphabet. Synergies were extracted from a subset of training postures using non-negative matrix factorization and used to predict the EMG patterns of the remaining postures. Across the subject population, as few as 11 postures could form an eight-dimensional synergy framework that allowed for at least 90% prediction of the EMG patterns of all 33 postures, including trial-to-trial variations. Synergies were quite robust despite using different postures in the training set, and also despite using a varied number of postures. Estimated synergies were categorized into those which were subject-specific and those which were general to the population. Population synergies were sparser than the subject-specific synergies, typically being dominated by a single muscle. Subject-specific synergies were more balanced in the coactivation of multiple muscles. We suggest as a result that global muscle coordination may be a combination of higher order control of robust subject-specific muscle synergies and lower order control of individuated muscles, and that this control paradigm may be useful in the control of EMG-based technologies, such as artificial limbs and functional electrical stimulation systems.