SEMG-based prediction of masticatory kinematics in rhythmic clenching movements

Kalani, Hadi; Moghimi, Sahar; Akbarzadeh, Alireza

doi:10.1016/j.bspc.2015.04.003

Cited by 19 publications

(1 citation statement)

References 43 publications

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“…A hybrid time-delayed artificial neural network was investigated to predict clenching movements during mastication from sEMG signals. Actual jaw motions and sEMG signals from the masticatory muscles were recorded and used as output and input, respectively, [19]. Muceli and Farina also adopted multiplayer perceptron (MLP) artificial neural networks to estimate kinematics of the hand wrist from EMG signals of the contralateral limb during mirrored bilateral movements in free space [20].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%