A Method for Online Optimization of Lower Limb Assistive Devices with High Dimensional Parameter Spaces
A Method for Online Optimization of Lower Limb Assistive Devices with High Dimensional Parameter Spaces
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DOI：
10.1109/icra.2018.8460953
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Nitish Thatte;Helei Duan;H. Geyer
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作者：
Nitish Thatte;Helei Duan;H. Geyer
我们提出了一种方法来优化辅助下肢设备的控制策略。该方法的框架参数选择作为一个决斗土匪的问题，其中用户表示他或她的定性偏好对参数集从库中选择。我们通过离线优化程序生成库，该程序旨在重现健康人类受试者的各种步态。通过将参数选择过程分为在线和离线部分，该方法可以处理高维参数空间，并产生可以推广到不同步态场景（如速度变化）的策略。我们评估的方法上行走的动力膝关节和踝关节假体的神经肌肉控制政策，有43个参数。我们发现，五个主题的首选四个不同的参数集从图书馆，并得到最佳类似完整的受试者步态数据。该结果表明，优化方法的离线部分确实产生了可以适应不同步态的控制参数。此外，我们发现，对于我们测试的四个参数集中的三个，该过程还生成了通过增加踝关节净生产来提高假体适应步态速度增加的能力的参数。这些结果鼓励在临床环境中进一步研究和探索采用在线学习的先进假体控制。
We propose a method for optimizing control policies for assistive lower-limb devices. The method frames parameter selection as a dueling bandits problem in which a user indicates his or her qualitative preferences between pairs of parameter sets chosen from a library. We generate the library through an offline optimization procedure that seeks to reproduce the varied gaits of healthy human subjects. By separating the parameter selection process into online and offline portions, the method can handle high-dimensional parameter spaces and produces policies that can generalize to different gait scenarios such as speed variation. We evaluate the method on five subjects walking on a powered knee and ankle prosthesis governed by a neuromuscular control policy that has 43 parameters. We find the five subjects preferred four different parameter sets from the library and that the resulting optima resemble intact subject gait data. This result suggests the offline portion of the optimization method indeed produces control parameters that can adapt to different gaits. Moreover, we find that for three out of the four parameter sets we tested, the procedure also generates parameters that improve the ability of the prosthesis to adapt to increasing gait speed by increasing ankle net work production. The results encourage further research and exploration in clinical settings toward advanced prosthesis controls that employ online learning.