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Journal of Sensors
Volume 2017, Article ID 7360953, 18 pages
Research Article

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

1School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
2Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China
3National Key Laboratory of Aerospace Flight Dynamics, School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China
4Images, Signals and Intelligence Systems Laboratory (LISSI/EA 3956), Senart-Fontainebleau Institute of Technology, UPEC, Bât. A, 77127 Lieusaint, France

Correspondence should be addressed to Weiwei Yu; nc.ude.upwn@iewiewuy

Received 11 March 2017; Revised 5 June 2017; Accepted 4 July 2017; Published 29 August 2017

Academic Editor: Xiaokun Zhang

Copyright © 2017 Weiwei Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.