Department of Mechanical Engineering, University of Michigan, 2350 Hayward, Ann Arbor, MI, 48109, USA.
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Gainesville, FL, 32611, USA.
J Neuroeng Rehabil. 2018 May 25;15(1):42. doi: 10.1186/s12984-018-0379-6.
Controllers for assistive robotic devices can be divided into two main categories: controllers using neural signals and controllers using mechanically intrinsic signals. Both approaches are prevalent in research devices, but a direct comparison between the two could provide insight into their relative advantages and disadvantages. We studied subjects walking with robotic ankle exoskeletons using two different control modes: dynamic gain proportional myoelectric control based on soleus muscle activity (neural signal), and timing-based mechanically intrinsic control based on gait events (mechanically intrinsic signal). We hypothesized that subjects would have different measures of metabolic work rate between the two controllers as we predicted subjects would use each controller in a unique manner due to one being dependent on muscle recruitment and the other not.
The two controllers had the same average actuation signal as we used the control signals from walking with the myoelectric controller to shape the mechanically intrinsic control signal. The difference being the myoelectric controller allowed step-to-step variation in the actuation signals controlled by the user's soleus muscle recruitment while the timing-based controller had the same actuation signal with each step regardless of muscle recruitment.
We observed no statistically significant difference in metabolic work rate between the two controllers. Subjects walked with 11% less soleus activity during mid and late stance and significantly less peak soleus recruitment when using the timing-based controller than when using the myoelectric controller. While walking with the myoelectric controller, subjects walked with significantly higher average positive and negative total ankle power compared to walking with the timing-based controller.
We interpret the reduced ankle power and muscle activity with the timing-based controller relative to the myoelectric controller to result from greater slacking effects. Subjects were able to be less engaged on a muscle level when using a controller driven by mechanically intrinsic signals than when using a controller driven by neural signals, but this had no affect on their metabolic work rate. These results suggest that the type of controller (neural vs. mechanical) is likely to affect how individuals use robotic exoskeletons for therapeutic rehabilitation or human performance augmentation.
辅助机器人设备的控制器可分为两类:使用神经信号的控制器和使用机械固有信号的控制器。这两种方法在研究设备中都很常见,但对两者进行直接比较可以深入了解它们的相对优势和劣势。我们使用两种不同的控制模式研究了使用机器人踝部外骨骼行走的受试者:基于比目鱼肌活动的动态增益比例肌电控制(神经信号)和基于步态事件的基于时间的机械固有控制(机械固有信号)。我们假设由于一个控制器依赖于肌肉募集,而另一个不依赖于肌肉募集,因此两个控制器之间的代谢功率会有所不同。
两个控制器具有相同的平均致动信号,因为我们使用带有肌电控制器行走的控制信号来塑造机械固有控制信号。不同之处在于,肌电控制器允许用户的比目鱼肌募集控制致动信号的逐步变化,而基于时间的控制器则无论肌肉募集情况如何,每一步都具有相同的致动信号。
我们没有观察到两种控制器之间代谢功率有统计学上的显著差异。与使用肌电控制器相比,使用基于时间的控制器时,受试者在中末期和末期的比目鱼肌活动减少了 11%,并且比目鱼肌募集的峰值明显减少。当使用肌电控制器时,受试者行走时的平均正、负总踝关节功率明显高于使用基于时间的控制器。
与肌电控制器相比,基于时间的控制器的踝关节功率和肌肉活动减少,我们认为这是由于松弛效应更大所致。与使用神经信号驱动的控制器相比,使用机械固有信号驱动的控制器时,受试者在肌肉水平上的参与程度降低,但这对他们的代谢功率没有影响。这些结果表明,控制器的类型(神经与机械)可能会影响个体使用机器人外骨骼进行治疗康复或人类性能增强的方式。