Abstract
In the case of dynamic motion such as jumping, an important fact in sEMG (surface Electromyogram) signal based control on exoskeletons, myoelectric prostheses, and rehabilitation gait is that multichannel sEMG signals contain mass data and vary greatly with time, which makes it difficult to generate compliant gait. Inspired by the fact that muscle synergies leading to dimensionality reduction may simplify motor control and learning, this paper proposes a new approach to generate flexible gait based on muscle synergies extracted from sEMG signal. Two questions were discussed and solved, the first one concerning whether the same set of muscle synergies can explain the different phases of hopping movement with various velocities. The second one is about how to generate self-adapted gait with muscle synergies while alleviating model sensitivity to sEMG transient changes. From the experimental results, the proposed method shows good performance both in accuracy and in robustness for producing velocity-adapted vertical jumping gait. The method discussed in this paper provides a valuable reference for the sEMG-based control of bionic robot leg to generate human-like dynamic gait.
Highlights
Surface Electromyogram is the electrical manifestation of muscular contractions, which reflexes plentiful neural control information
Since vertical jumping is the fundamental movement pattern of dynamic motion, such as jumping, bouncing, and running, based on the above discussion, we will focus on generating flexible gait of vertical jumping with Surface Electromyogram (sEMG) signals for biomechanical leg
If the number of synergies is chosen as four, the simulation results in Table 1 (Section 3.2) show that the variance accounted for (VAF) of all the trials is above 90%, which indicates that the recorded sEMGs are well reconstructed by the extracted synergies
Summary
Surface Electromyogram (sEMG) is the electrical manifestation of muscular contractions, which reflexes plentiful neural control information. An important factor presenting in the sEMG-based biomechanical leg control, during the dynamic motion such as jumping and running, is the fact that multichannel sEMG signals contain mass data and vary greatly with time. Since vertical jumping is the fundamental movement pattern of dynamic motion, such as jumping, bouncing, and running, based on the above discussion, we will focus on generating flexible gait of vertical jumping with sEMG signals for biomechanical leg. The advantages of this approach are the possibilities to generate a self-adaptive gait pattern of vertical jumping with limited number of experimental data, which is very meaningful for the sEMG-based robotic leg application
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