Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg's Control
Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg's Control
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DOI：
10.1155/2017/7360953
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
Weiwei Yu;Weihua Ma;Yangyang Feng;Runxiao Wang;K. Madani;C. Sabourin
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文献类型：
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作者：
Weiwei Yu;Weihua Ma;Yangyang Feng;Runxiao Wang;K. Madani;C. Sabourin
在跳跃等动态运动的情况下，基于表面肌电信号（sEMG）信号的外骨骼、肌电假肢和康复步态控制中的一个重要事实是，多通道sEMG信号包含大量数据并且随时间变化很大，这使得难以产生顺应步态。受肌肉协同效应降维可以简化运动控制和学习的启发，提出了一种基于表面肌电信号中肌肉协同效应的柔性步态生成方法。两个问题进行了讨论和解决，第一个是关于是否相同的肌肉协同作用可以解释不同阶段的跳跃运动与不同的速度。第二个是关于如何生成具有肌肉协同作用的自适应步态，同时减轻模型对sEMG瞬态变化的敏感性。从实验结果来看，该方法在产生速度适应的垂直跳跃步态的准确性和鲁棒性方面都表现出良好的性能。该方法为基于表面肌电信号的仿生机器人腿部控制生成类人动态步态提供了有价值的参考。
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.