Concurrent Contribution of Co-Contraction to Error Reduction During Dynamic Adaptation of the Wrist
Concurrent Contribution of Co-Contraction to Error Reduction During Dynamic Adaptation of the Wrist
复制标题
手腕动态适应期间共同收缩对减少误差的同时贡献
DOI：
10.1109/tnsre.2023.3242601
复制
发表时间：
2023
影响因子：
4.9
通讯作者：
Sergi, Fabrizio
中科院分区：
文献类型：
--
作者：
Farrens, Andria J.;Schmidt, Kristin;Cohen, Hannah;Sergi, Fabrizio
兼容MRI的机器人提供了一种研究复杂感觉运动学习过程（如适应）中大脑功能的方法。为了正确解释使用MRI兼容机器人测量的行为的神经相关性，验证通过此类设备获得的运动性能的测量结果至关重要。以前，我们的特点是适应的手腕，通过MRI兼容的机器人，MR-SoftWrist施加的力场。手臂达到的任务相比，我们观察到低端幅度的适应，并减少轨迹误差超出了适应解释。因此，我们形成了两个假设：观察到的差异是由于MR-SoftWrist的测量误差;或者阻抗控制在动态扰动期间对手腕运动的控制起着重要作用。为了检验这两个假设，我们进行了两个阶段的平衡交叉研究。在这两个会议中，参与者进行手腕指向三个力场条件（零力，恒定，随机）。参与者使用MR-SoftWrist或UDiffWrist（一种非MRI兼容的腕部机器人）在第一阶段执行任务，在第二阶段使用另一种设备。为了测量与阻抗控制相关的预期共同收缩，我们收集了四块前臂肌肉的表面肌电图。我们发现器械对行为没有显著影响，从而验证了使用MR-SoftWrist获得的适应性测量结果。EMG措施的共同收缩解释了一个显着的一部分，在过度的误差减少的方差不归因于适应。这些结果支持的假设，对于手腕，阻抗控制显着有助于减少轨迹误差超过那些解释的适应。
MRI-compatible robots provide a means of studying brain function involved in complex sensorimotor learning processes, such as adaptation. To properly interpret the neural correlates of behavior measured using MRI-compatible robots, it is critical to validate the measurements of motor performance obtained via such devices. Previously, we characterized adaptation of the wrist in response to a force field applied via an MRI-compatible robot, the MR-SoftWrist. Compared to arm reaching tasks, we observed lower end magnitude of adaptation, and reductions in trajectory errors beyond those explained by adaptation. Thus, we formed two hypotheses: that the observed differences were due to measurement errors of the MR-SoftWrist; or that impedance control plays a significant role in control of wrist movements during dynamic perturbations. To test both hypotheses, we performed a two-session counterbalanced crossover study. In both sessions, participants performed wrist pointing in three force field conditions (zero force, constant, random). Participants used either the MR-SoftWrist or the UDiffWrist, a non-MRI-compatible wrist robot, for task execution in session one, and the other device in session two. To measure anticipatory co-contraction associated with impedance control, we collected surface EMG of four forearm muscles. We found no significant effect of device on behavior, validating the measurements of adaptation obtained with the MR-SoftWrist. EMG measures of co-contraction explained a significant portion of the variance in excess error reduction not attributable to adaptation. These results support the hypothesis that for the wrist, impedance control significantly contributes to reductions in trajectory errors in excess of those explained by adaptation.