IEEE Trans Biomed Eng. 2024 Sep;71(9):2630-2641. doi: 10.1109/TBME.2024.3384340. Epub 2024 Aug 21.
Adaptation of upper-limb impeda- nce (stiffness, damping, inertia) is crucial for humans to physically interact with the external environment during grasping and manipulation tasks. Here, we present a novel framework for Adaptive Impedance Control of Upper-limb Prosthesis (AIC-UP) based on surface electromyography (sEMG) signals.
AIC-UP uses muscle-tendon models driven by sEMG signals from agonist-antagonist muscle groups to estimate the human motor intent as joint kinematics, stiffness and damping. These estimates are used to implement a variable impedance controller on a simulated robot. Designed for use by amputees, joint torque or stiffness measurements are not used for model calibration. AIC-UP was evaluated with eight able-bodied subjects and a transradial amputee performing target-reaching tasks in simulation through wrist flexion-extension. The control performance was tested in free space and in the presence of unexpected perturbations.
We show that AIC-UP outperformed a neural network that regresses the desired kinematics from sEMG signals, in terms of robustness to muscle coactivations needed to complete the task. These results were in agreement with the qualitative feedback from the participants. Additionally, we observed that AIC-UP enables the user to adapt the stiffness and damping to the tasks at hand.
在上肢与外部环境进行抓握和操作任务的物理交互过程中，上肢阻抗（刚度、阻尼、惯性）的适应性至关重要。本研究提出了一种基于表面肌电信号（sEMG）的上肢假肢自适应阻抗控制（AIC-UP）的新框架。
AIC-UP 使用来自拮抗肌群的 sEMG 信号驱动的肌肉肌腱模型来估计人类运动意图，作为关节运动学、刚度和阻尼。这些估计值用于在模拟机器人上实现可变阻抗控制器。为假肢使用者设计，无需进行关节扭矩或刚度测量来进行模型校准。AIC-UP 在通过腕关节屈伸进行模拟的 8 名健康受试者和一名桡骨截肢者中进行了评估，以完成目标到达任务。在自由空间和存在意外干扰的情况下测试了控制性能。
我们表明，AIC-UP 在完成任务所需的肌肉协同激活方面的稳健性方面，优于从 sEMG 信号回归期望运动学的神经网络。这些结果与参与者的定性反馈一致。此外，我们观察到 AIC-UP 使使用者能够根据手头的任务调整刚度和阻尼。
