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基于比目鱼肌模型的踝关节外骨骼扭矩控制器。

Ankle exoskeleton torque controllers based on soleus muscle models.

机构信息

Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, United States of America.

Industrial and Operations Engineering, Robotics Institute, University of Michigan, Ann Arbor, MI, United States of America.

出版信息

PLoS One. 2023 Feb 27;18(2):e0281944. doi: 10.1371/journal.pone.0281944. eCollection 2023.

Abstract

Powered exoskeletons are typically task-specific, but to facilitate their wider adoption they should support a variety of tasks, which requires generalizeable controller designs. In this paper, we present two potential controllers for ankle exoskeletons based on soleus fascicles and Achilles tendon models. The methods use an estimate of the adenosine triphosphate hydrolysis rate of the soleus based on fascicle velocity. Models were evaluated using muscle dynamics from the literature, which were measured with ultrasound. We compare the simulated behavior of these methods against each other and to human-in-the-loop optimized torque profiles. Both methods generated distinct profiles for walking and running with speed variations. One of the approaches was more appropriate for walking, while the other approach estimated profiles similar to the literature for both walking and running. Human-in-the-loop methods require long optimizations to set parameters per individual for each specific task, the proposed methods can produce similar profiles, work across walking and running, and be implemented with body-worn sensors without requiring torque profile parameterization and optimization for every task. Future evaluations should examine how human behavior changes due to external assistance when using these control models.

摘要

动力外骨骼通常是特定于任务的,但为了促进它们更广泛的采用,它们应该支持各种任务,这需要可推广的控制器设计。在本文中,我们提出了两种基于比目鱼肌纤维束和跟腱模型的踝关节外骨骼的潜在控制器。这些方法使用基于纤维束速度的比目鱼肌三磷酸腺苷水解率的估计值。使用来自文献中的超声测量的肌肉动力学对模型进行了评估。我们将这些方法的模拟行为相互进行了比较,并与人类在回路中优化的扭矩曲线进行了比较。这两种方法都针对步行和跑步的速度变化生成了不同的曲线。其中一种方法更适合步行,而另一种方法则针对步行和跑步都估计出了类似于文献中的曲线。人类在回路中的方法需要针对每个特定任务对每个个体进行长时间的优化来设置参数,而所提出的方法可以产生类似的曲线,适用于步行和跑步,并且可以使用佩戴在身上的传感器来实现,而无需针对每个任务进行扭矩曲线参数化和优化。未来的评估应该研究当使用这些控制模型时,由于外部辅助,人类行为会发生怎样的变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b081/9970081/4e3b731019be/pone.0281944.g001.jpg

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