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

OBJECTIVE: Proportional and simultaneous est-imation 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.

An Approach to Extract Nonlinear Muscle Synergies from sEMG through Multi-Model Learning.

How does the Central Nervous System (CNS) controls a group of muscles is an important question in the field of motor control. A common conception is developed over the years that the CNS make use of predefined activation patterns, known as muscle synergies during task execution. These muscle synergies are extracted by applying any of the factorization algorithms such as Non-Negative Matrix Factorization (NNMF), Independent Component Analysis (ICA) or Principle Component Analysis (PCA) on a concatenated surface EMG data set recorded from the target muscles. However, the step to concatenate sEMG signals before they are given as input to these linear algorithm is crucial as the synergistic structure changes significantly based on the number and choice of muscles considered during concatenation step. To address this problem, we propose a new approach of extracting muscle synergies by treating sEMG signals from each muscle as an individual modality and then learning the synergistic structure among them if it exists using multi-view learning. In this study, we propose to use Manifold Relevance Determination (MRD) to find nonlinear synergies from sEMG by assuming the sEMG of a muscle as an individual modality. Results have shown that synergistic patterns extracted using our approach are consistent upon addition of sEMG signals from new muscles.