Surface electromyography (sEMG) signal has been used in upper limb rehabilitation robots (ULRR). However, existing ULRR based on myoelectric controllers suffers from limited generalization ability in estimating three-dimensional (3-D) motion intention. This article proposes a muscle-synergy-inspired approach to enhance the generalization ability of the myoelectric controller of a cable-driven ULRR. Low-dimensional commands are extracted from sEMG signals based on an EMG-to-muscle activation model and non-negative matrix factorization. The extracted commands are used to estimate the 3-D human force. Two different trajectory tracking tasks are selected to test the generalization ability. The system is trained based on training sets where participants perform one task. Then the system is tested using testing sets where participants perform the other task. Finally, the system is verified on real-time robotic control experiment. Results show that the proposed controller achieves better force estimating accuracy, better trajectory tracking accuracy, and lower interaction force than the myoelectric controller without considering muscle synergies, which means the proposed controller yields better generalization performance.

中文翻译：

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具有基于肌肉协同的肌电控制器的电缆驱动上肢康复机器人


表面肌电图（sEMG）信号已用于上肢康复机器人（ULRR）。然而，现有的基于肌电控制器的 ULRR 在估计三维（3-D）运动意图方面的泛化能力有限。本文提出了一种受肌肉协同启发的方法来增强电缆驱动 ULRR 肌电控制器的泛化能力。基于 EMG 到肌肉激活模型和非负矩阵分解，从 sEMG 信号中提取低维命令。提取的命令用于估计 3D 人力。选择两个不同的轨迹跟踪任务来测试泛化能力。该系统是根据参与者执行一项任务的训练集进行训练的。然后使用测试集对系统进行测试，参与者在其中执行其他任务。最后通过实时机器人控制实验对系统进行了验证。结果表明，在不考虑肌肉协同作用的情况下，所提出的控制器比肌电控制器实现了更好的力估计精度、更好的轨迹跟踪精度和更低的交互力，这意味着所提出的控制器产生了更好的泛化性能。