优化控制映射通过用户调整成本的努力,时间和可靠性*

Anjana Gayathri Arunachalam, K. Englehart, J. Sensinger
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引用次数: 1

摘要

人类总是通过协调关节来完成各种各样的任务。计算电机控制理论用最优控制解释了这些刻板行为。有几个成本函数被用来解释特定的动作,这表明大脑对成本的组合进行优化,只是改变它们的相对权重来执行不同的任务。在可调人机界面的情况下,我们假设人机界面应该根据用户在进行运动时关心的成本进行优化。在这里,我们研究了复合运动成本中单个成本函数的相对权重如何影响用户产生的最优控制信号以及用户控制信号与机器输出之间的映射,并以假肢控制为例。通过建立分层优化模型对该框架进行了验证,该模型对用户控制信号和设备的虚拟动力学进行了独立优化。我们的结果表明了该方法的可行性,并显示了在假体调谐中使用这种模型的潜力。这种方法可以让临床医生和用户根据他们真正关心的成本来调整他们的假肢;并允许平台根据每个患者的独特需求进行定制。
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Optimized control mapping through user-tuned cost of effort, time, and reliability*
Humans consistently coordinate their joints to perform a variety of tasks. Computational motor control theory explains these stereotypical behaviors using optimal control. Several cost functions have been used to explain specific movements, which suggests that the brain optimizes for a combination of costs and just varies their relative weights to perform different tasks. In the case of tunable human-machine interfaces, we hypothesize that the human-machine interface should be optimized according to the costs that the user cares about when making the movement. Here, we study how the relative weights of individual cost functions in a composite movement cost affect the optimal control signal produced by the user and the mapping between the user’s control signals and the machine’s output, using prosthesis control as a specific example. This framework was tested by building a hierarchical optimization model that independently optimized for the user control signal and the virtual dynamics of the device. Our results indicate the feasibility of the approach and show the potential for using such a model in prosthesis tuning. This method could be used to allow clinicians and users to tune their prosthesis based on costs they actually care about; and allow the platforms to be customized for the unique needs of every patient.
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