Department of Bioengineering, George Mason University, Fairfax, VA, 22030, USA.
Department of Psychology, George Mason University, Fairfax, VA, 22030, USA.
Sci Rep. 2023 Aug 15;13(1):13273. doi: 10.1038/s41598-023-40446-5.
There have been significant advances in biosignal extraction techniques to drive external biomechatronic devices or to use as inputs to sophisticated human machine interfaces. The control signals are typically derived from biological signals such as myoelectric measurements made either from the surface of the skin or subcutaneously. Other biosignal sensing modalities are emerging. With improvements in sensing modalities and control algorithms, it is becoming possible to robustly control the target position of an end-effector. It remains largely unknown to what extent these improvements can lead to naturalistic human-like movement. In this paper, we sought to answer this question. We utilized a sensing paradigm called sonomyography based on continuous ultrasound imaging of forearm muscles. Unlike myoelectric control strategies which measure electrical activation and use the extracted signals to determine the velocity of an end-effector; sonomyography measures muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Previously, we showed that users were able to accurately and precisely perform a virtual target acquisition task using sonomyography. In this work, we investigate the time course of the control trajectories derived from sonomyography. We show that the time course of the sonomyography-derived trajectories that users take to reach virtual targets reflect the trajectories shown to be typical for kinematic characteristics observed in biological limbs. Specifically, during a target acquisition task, the velocity profiles followed a minimum jerk trajectory shown for point-to-point arm reaching movements, with similar time to target. In addition, the trajectories based on ultrasound imaging result in a systematic delay and scaling of peak movement velocity as the movement distance increased. We believe this is the first evaluation of similarities in control policies in coordinated movements in jointed limbs, and those based on position control signals extracted at the individual muscle level. These results have strong implications for the future development of control paradigms for assistive technologies.
在提取生物信号以驱动外部生物机电设备或用作复杂人机界面的输入方面，已经取得了重大进展。控制信号通常来自生物信号，例如从皮肤表面或皮下进行的肌电测量。其他生物信号感测方式也在不断涌现。随着感测方式和控制算法的改进，已经可以稳健地控制末端执行器的目标位置。但是，这些改进在多大程度上可以导致类似自然的人类运动，仍然知之甚少。在本文中，我们试图回答这个问题。我们利用了一种称为声触诊弹性成像的传感范例，该范例基于对前臂肌肉的连续超声成像。与测量电激活并使用提取的信号来确定末端执行器速度的肌电控制策略不同；声触诊弹性成像直接使用超声测量肌肉变形，并使用提取的信号成比例地控制末端执行器的位置。以前，我们已经证明用户可以使用声触诊弹性成像准确，精确地执行虚拟目标获取任务。在这项工作中，我们研究了声触诊弹性成像衍生控制轨迹的时间过程。我们表明，用户到达虚拟目标的声触诊弹性成像衍生轨迹的时间过程反映了被证明是生物肢体运动学特征典型的轨迹。具体来说，在目标获取任务中，速度曲线遵循最小冲击轨迹，该轨迹适用于点对点手臂伸展运动，到达目标的时间相似。此外，基于超声成像的轨迹导致随着运动距离的增加，峰值运动速度的系统延迟和缩放。我们相信，这是首次评估关节肢体协调运动中基于位置控制信号的关节运动控制策略的相似性。这些结果对辅助技术的控制范式的未来发展具有重要意义。
