Muscle co-contraction patterns in robot-mediated force field learning to guide specific muscle group training
Pizzamiglio, Sara, Desowska, Adela, Shojaii, Pegah, Taga, Myriam and Turner, D. 2017. Muscle co-contraction patterns in robot-mediated force field learning to guide specific muscle group training. NeuroRehabilitation. 41 (1), pp. 17-29.
|Authors||Pizzamiglio, Sara, Desowska, Adela, Shojaii, Pegah, Taga, Myriam and Turner, D.|
BACKGROUND: Muscle co-contraction is a strategy of increasing movement accuracy and stability employed in dealing with perturbation of movement. It is often seen in neuropathological populations. The direction of movement influences the pattern of co-contraction, but not all movements are easily achievable for populations with motor deficits. Manipulating the direction of the force instead, may be a promising rehabilitation protocol to train movement with use of a co-contraction reduction strategy. Force field learning paradigms provide a well described procedure to evoke and test muscle co-contraction.
OBJECTIVE: The aim of this study was to test the muscle co-contraction pattern in a wide range of arm muscles in different force-field directions utilising a robot-assisted force field learning paradigm of motor adaptation.
METHOD: 42 participants volunteered to participate in a study utilising robot-assisted motor adaptation paradigm with clockwise or counter-clockwise force field. Kinematics and surface electromyography (EMG) of eight arm muscles has been measured.
RESULTS: Both muscle activation and co-contraction was earlier and stronger in flexors in clockwise condition and in extensors in the counter-clockwise condition.
CONCLUSIONS: Manipulating the force field direction leads to changes in the pattern of muscle co-contraction.
|Keywords||Motor adaptation; force-field learning; EMG; co-contraction; rehabilitation|
|Journal citation||41 (1), pp. 17-29|
|Accepted author manuscript|
|Digital Object Identifier (DOI)||doi:10.3233/NRE-171453|
|22 Jul 2017|
|Publication process dates|
|Deposited||09 May 2017|
|Accepted||30 Jan 2017|
|Copyright information||© 2017 The authors. The final publication is available at IOS Press through http://dx.doi.org/10.3233/NRE-171453|
|License||All rights reserved|
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