A Method for Online Optimization of Lower Limb Assistive Devices with High Dimensional Parameter Spaces

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.