Muscle synergies as a predictive framework for the EMG patterns of new hand postures.
Muscle synergies as a predictive framework for the EMG patterns of new hand postures.
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肌肉协同作用是新手姿势的EMG模式的预测框架。
DOI:
10.1088/1741-2560/6/3/036004
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发表时间:
2009-06
影响因子:
4
通讯作者:
Weir RF
中科院分区:
文献类型:
--
作者:
Ajiboye AB;Weir RF
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.
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影响因子:
5.3
作者:
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通讯作者:
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通讯作者:
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作者:
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通讯作者:
Sejnowski, TJ