"Body-In-The-Loop": Optimizing Device Parameters Using Measures of Instantaneous Energetic Cost.
"Body-In-The-Loop": Optimizing Device Parameters Using Measures of Instantaneous Energetic Cost.
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DOI：
10.1371/journal.pone.0135342
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Remy CD
中科院分区：
文献类型：
--
作者：
Felt W;Selinger JC;Donelan JM;Remy CD
本文演示了辅助机器人设备，如动力假肢，矫形器和外骨骼的在线优化方法。我们的算法实时估计生理目标的值（身体“在环中”），并使用此信息来确定最佳设备参数。为了处理有噪声和动态延迟的传感器数据，我们依赖于动态估计和响应面识别的组合。我们评估了三种算法（稳态成本映射，瞬时成本映射和瞬时成本梯度搜索）与8名健康的人类受试者。稳态成本映射是一种成熟的技术，它将三次多项式拟合到不同参数设置下稳态测量的平均值。最佳参数值由多项式拟合确定。通过在一系列参数上连续扫描并考虑测量动态，瞬时成本映射可以更快地识别三次多项式。瞬时成本梯度搜索使用类似的技术来使用局部梯度的估计迭代地接近最优参数值。为了以简单和可重复的方式评估这些方法，我们通过节拍器规定步频并优化该频率以最大限度地减少代谢能量成本。这种步进频率的使用允许将我们的结果与已建立的技术进行比较，并使其他人能够复制我们的方法。我们的结果表明，这三种方法在估计最佳步频方面都达到了相似的准确度。对于所有方法，预测的最小值和受试者的首选步频之间的平均误差小于1%，标准差在4%和5%之间。使用即时成本映射，我们能够将受试者的步行时间从一个多小时减少到不到10分钟。虽然对于单个参数，瞬时成本梯度搜索并不比稳态成本映射快得多，但瞬时成本梯度搜索有利地扩展到多维参数空间。
This paper demonstrates methods for the online optimization of assistive robotic devices such as powered prostheses, orthoses and exoskeletons. Our algorithms estimate the value of a physiological objective in real-time (with a body “in-the-loop”) and use this information to identify optimal device parameters. To handle sensor data that are noisy and dynamically delayed, we rely on a combination of dynamic estimation and response surface identification. We evaluated three algorithms (Steady-State Cost Mapping, Instantaneous Cost Mapping, and Instantaneous Cost Gradient Search) with eight healthy human subjects. Steady-State Cost Mapping is an established technique that fits a cubic polynomial to averages of steady-state measures at different parameter settings. The optimal parameter value is determined from the polynomial fit. Using a continuous sweep over a range of parameters and taking into account measurement dynamics, Instantaneous Cost Mapping identifies a cubic polynomial more quickly. Instantaneous Cost Gradient Search uses a similar technique to iteratively approach the optimal parameter value using estimates of the local gradient. To evaluate these methods in a simple and repeatable way, we prescribed step frequency via a metronome and optimized this frequency to minimize metabolic energetic cost. This use of step frequency allows a comparison of our results to established techniques and enables others to replicate our methods. Our results show that all three methods achieve similar accuracy in estimating optimal step frequency. For all methods, the average error between the predicted minima and the subjects’ preferred step frequencies was less than 1% with a standard deviation between 4% and 5%. Using Instantaneous Cost Mapping, we were able to reduce subject walking-time from over an hour to less than 10 minutes. While, for a single parameter, the Instantaneous Cost Gradient Search is not much faster than Steady-State Cost Mapping, the Instantaneous Cost Gradient Search extends favorably to multi-dimensional parameter spaces.