Inverse Estimation of Multiple Muscle Activations From Joint Moment With Muscle Synergy Extraction

Abstract

Human movement is produced resulting from synergetic combinations of multiple muscle contractions. The resultant joint movement can be estimated through the related multiple-muscle activities, which is formulated as the forward problem. Neuroprosthetic applications may benefit from cocontraction of agonist and antagonist muscle pairs to achieve more stable and robust joint movements. It is necessary to estimate the activation of each individual muscle from desired joint torque(s), which is the inverse problem. A synergy-based solution is presented for the inverse estimation of multiple muscle activations from joint movement, focusing on one degree-of-freedom tasks. The approach comprises muscle synergy extraction via the nonnegative matrix factorization algorithm. Cross validation is performed to evaluate the method for prediction accuracy based on experimental data from ten able-bodied subjects. The results demonstrate that the approach succeeds to inversely estimate the multiple muscle activities from the given joint torque sequence. In addition, the other one's averaged synergy ratio was applied for muscle activation estimation with leave-one-out cross-validation manner, which resulted in 9.3% estimation error over all the subjects. The obtained results support the common muscle synergy-based neuroprosthetics control concept.

Real-time closed-loop FES control of muscle activation with evoked EMG feedback

“…Accurate modeling would result in good control performance, especially for model predictive control to provide feedforward control nature. If this scheme is combined with the muscle synergy pattern extraction in inverse problem [12], the natural multiple muscle patterns would be able to be specified in the paralyzed muscles. Such multiple muscle control could contribute to joint stiffness control, by means of artificially adopting the muscle co-contraction based on the corresponding synergy pattern.…”

Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model

“…Compared with the prediction in "open-loop" form, the closed-loop estimation would provide more accurate results. Additionally, it should be noted that the state-space regression model not only involves the forward prediction of joint-movements from muscle activities; but also involves its inverse process of reconstructing muscle activities by joint-movements, which has been investigated for neurosystem applications by some studies [28], [29].…”

A hybrid functional electrical stimulation for real-time estimation of joint torque and closed-loop control of muscle activation

“…Clinical experiment results on SCI subjects verify its feasibility and efficiency. Future work could be extended to applying such FES system on estimation/control issues of multiple muscles/joints with muscle/joint synergy considered 10 , and comparison of M-wave during dynamic contractions and during isometric contractions is also worth investigating.…”