A model-based approach to predict muscle synergies using optimization: application to feedback control
| dc.contributor.author | Sharif Razavian, Reza | |
| dc.contributor.author | Mehrabi, Naser | |
| dc.contributor.author | McPhee, John | |
| dc.date.accessioned | 2017-03-16T18:53:04Z | |
| dc.date.available | 2017-03-16T18:53:04Z | |
| dc.date.issued | 2015-10-06 | |
| dc.description | This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission. | en |
| dc.description.abstract | This paper presents a new model-based method to define muscle synergies. Unlike the conventional factorization approach, which extracts synergies from electromyographic data, the proposed method employs a biomechanical model and formally defines the synergies as the solution of an optimal control problem. As a result, the number of required synergies is directly related to the dimensions of the operational space. The estimated synergies are posture-dependent, which correlate well with the results of standard factorization methods. Two examples are used to showcase this method: a two-dimensional forearm model, and a three-dimensional driver arm model. It has been shown here that the synergies need to be task-specific (i.e., they are defined for the specific operational spaces: the elbow angle and the steering wheel angle in the two systems). This functional definition of synergies results in a low-dimensional control space, in which every force in the operational space is accurately created by a unique combination of synergies. As such, there is no need for extra criteria (e.g., minimizing effort) in the process of motion control. This approach is motivated by the need for fast and bio-plausible feedback control of musculoskeletal systems, and can have important implications in engineering, motor control, and biomechanics. | en |
| dc.description.sponsorship | The authors wish to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for funding this study. | en |
| dc.identifier.uri | https://dx.doi.org/10.3389%2Ffncom.2015.00121 | |
| dc.identifier.uri | http://hdl.handle.net/10012/11518 | |
| dc.language.iso | en | en |
| dc.publisher | Frontiers Media | en |
| dc.rights | Attribution 4.0 International | * |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Muscle synergy | en |
| dc.subject | Real-time control | en |
| dc.subject | Model-based approach | en |
| dc.subject | Optimization | en |
| dc.subject | Operational space | en |
| dc.subject | Task-specific | en |
| dc.subject | Dynamic redundancy | en |
| dc.subject | Unique solution | en |
| dc.title | A model-based approach to predict muscle synergies using optimization: application to feedback control | en |
| dc.type | Article | en |
| dcterms.bibliographicCitation | Sharif Razavian, R., Mehrabi, N., & McPhee, J. (2015). A model-based approach to predict muscle synergies using optimization: application to feedback control. Frontiers in Computational Neuroscience, 9. https://doi.org/10.3389/fncom.2015.00121 | en |
| uws.contributor.affiliation1 | Faculty of Engineering | en |
| uws.contributor.affiliation2 | Systems Design Engineering | en |
| uws.peerReviewStatus | Reviewed | en |
| uws.scholarLevel | Faculty | en |
| uws.typeOfResource | Text | en |