Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg’s Control

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 velocityadapted 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.

Low limb prostheses and complex human prosthetic interaction: A systematic literature review

“… •sEMG: Signals are detected with sensors placed on the muscle surface, usually with two or more electrodes since it measures the electrical difference between activated muscle and a reference point ( Cabral et al., 2018 ). Several studies ( Luu et al., 2017 ; Yu et al., 2017 ; Su et al., 2019 ; Peng et al., 2020b ) use the described method of interaction due to its almost instant response, non-invasive technology, and ease of use. However, the disadvantage of this method is the noise produced by close muscles ( Nieveen et al., 2020 ), so it requires an analysis of the residual limb activation when performing different activities ( Gupta and Agarwal, 2019 ; Su et al., 2019 ; Wang et al., 2022 ).…”

Feature Extraction and Classification of Lower Limb Motion Based on sEMG Signals

“…It contains much information about muscle activity and has the advantages of noninvasive recording [1]. In recent years, it has become a research hotspot in humanmachine integrated intelligent equipment [2].…”