Extraction of Nonlinear Synergies for Proportional and Simultaneous Estimation of Finger Kinematics

Objective: Proportional and simultaneous estimation of finger kinematics from surface EMG based on the assumption that there exists a correlation between muscle activations and finger kinematics in low dimensional space. Methods: We employ Manifold Relevance Determination (MRD), a multi-view learnin...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2020-09, Vol.67 (9), p.2646-2658
Main Authors: Dwivedi, Sanjay Kumar, Ngeo, Jimson, Shibata, Tomohiro
Format: Article
Language:English
Subjects:
Activation
Bayesian analysis
Brain modeling
Correlation
Correlation coefficient
Correlation coefficients
Electromyography
Estimation
kinematic synergies
Kinematics
Machine learning
manifold relevance determination (MRD)
Motor task performance
Muscle contraction
Muscle synergies
Muscles
Myoelectric control
Myoelectricity
proportional
Robots
simultaneous myoelectric control
Task analysis
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Summary:Objective: Proportional and simultaneous estimation of finger kinematics from surface EMG based on the assumption that there exists a correlation between muscle activations and finger kinematics in low dimensional space. Methods: We employ Manifold Relevance Determination (MRD), a multi-view learning model with a nonparametric Bayesian approach, to extract the nonlinear muscle and kinematics synergies and the relationship between them by studying muscle activations (input-space) together with the finger kinematics (output-space). Results: This study finds that there exist muscle synergies which are associated with kinematic synergies. The acquired nonlinear synergies and the association between them has further been utilized for the estimation of finger kinematics from muscle activation inputs, and the proposed approach has outperformed other commonly used linear and nonlinear regression approaches with an average correlation coefficient of 0.91 ± 0.03. Conclusion: There exists an association between muscle and kinematic synergies which can be used for the proportional and simultaneous estimation of finger kinematics from the muscle activation inputs. Significance: The findings of this study not only presents a viable approach for accurate and intuitive myoelectric control but also provides a new perspective on the muscle synergies in the motor control community.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2020.2967154