IEEE Trans Neural Syst Rehabil Eng. 2019 Feb;27(2):314-322. doi: 10.1109/TNSRE.2019.2894464. Epub 2019 Jan 23.
In proportional myographic control, one can control either position or velocity of movement. Here, we propose to use adaptive auto-regressive filters, so as to gradually adjust between the two. We implemented this in an adaptive system with closed-loop feedback, where both the user and the machine simultaneously attempt to follow a cursor on a 2-D arena. We tested this on 15 able-bodied and three limb-deficient participants using an eight-channel myoelectric armband. The human-machine pairs learn to perform smoother cursor movements with a larger range of motion when using the auto-regressive filters, as compared with our previous effortswithmoving-average filters. Importantly, the human-machine system converges to an approximate velocity control strategy resulting in faster and more accuratemovements with lessmuscle effort. The method is not specific tomyoelectriccontroland could be used equally well for motion control using high-dimensional signals from reinnervatedmuscles or direct brain recordings.
在比例肌电控制中，人们可以控制运动的位置或速度。在这里，我们提出使用自适应自回归滤波器，以便在两者之间逐渐进行调整。我们在具有闭环反馈的自适应系统中实现了这一点，在该系统中，用户和机器同时尝试在二维竞技场中跟随光标。我们使用八通道肌电臂带在 15 名健全人和 3 名肢体残缺的参与者身上对此进行了测试。与我们之前使用移动平均滤波器的努力相比，当使用自回归滤波器时，人机对可以学习执行更平滑的光标移动，并且运动范围更大。重要的是，人机系统收敛到近似的速度控制策略，从而使运动更快，更准确，并且肌肉用力更少。该方法不仅限于肌电控制，同样可以很好地用于使用来自重新支配的肌肉或直接大脑记录的高维信号进行运动控制。
