Estimate the Kinematics with EMG Signal Using Fuzzy Wavelet Neural Network for Biomechanical Leg Application

Wang, Yu; Feng, Yangyang; Liang, Weiyu; Wang, Runxiao; Madani, Kurosh

doi:10.1007/978-3-319-41009-8_14

Cited by 2 publications

(1 citation statement)

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“…The number of synergies that can explain the muscle coactivation pattern efficiently was discussed in our previous work in [31]. Take two experimental trials as examples (uniform velocity jumping and variable velocity jumping).…”

Section: Muscle Synergies For Stance Phase Of Vertical Jumpingmentioning

confidence: 99%

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

Wang

Feng

et al. 2017

Journal of Sensors

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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.

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Section: Muscle Synergies For Stance Phase Of Vertical Jumpingmentioning

confidence: 99%