PDF(Pubmed)
Abstract：
Introduction: Human-in-the-loop optimization algorithms have proven useful in optimizing complex interactive problems, such as the interaction between humans and robotic exoskeletons. Specifically, this methodology has been proven valid for reducing metabolic cost while wearing robotic exoskeletons. However, many prostheses and orthoses still consist of passive elements that require manual adjustments of settings. Methods: In the present study, we investigated if human-in-the-loop algorithms could guide faster manual adjustments in a procedure similar to fitting a prosthesis. Eight healthy participants wore a prosthesis simulator and walked on a treadmill at 0.8 ms-1 under 16 combinations of shoe heel height and pylon height. A human-in-the-loop optimization algorithm was used to find an optimal combination for reducing the loading rate on the limb contralateral to the prosthesis simulator. To evaluate the performance of the optimization algorithm, we used a convergence criterium. We evaluated the accuracy by comparing it against the optimum from a full sweep of all combinations. Results: In five out of the eight participants, the human-in-the-loop optimization reduced the time taken to find an optimal combination; however, in three participants, the human-in-the-loop optimization either converged by the last iteration or did not converge. Discussion: Findings from this study show that the human-in-the-loop methodology could be helpful in tasks that require manually adjusting an assistive device, such as optimizing an unpowered prosthesis. However, further research is needed to achieve robust performance and evaluate applicability in persons with amputation wearing an actual prosthesis.
摘要：
简介：人在环优化算法已被证明在优化复杂的交互问题中很有用，比如人类和机器人外骨骼之间的相互作用。具体来说,这种方法已被证明是有效的降低代谢成本，而穿着机器人外骨骼。然而,许多假肢和矫形器仍然由被动元件组成，需要手动调整设置。方法：在本研究中，我们调查了在类似于安装假体的手术中,人工在环算法是否可以指导更快的手动调整.八名健康的参与者穿着假肢模拟器，在鞋跟高度和塔架高度的16种组合下以0.8ms-1的速度在跑步机上行走。使用人在环优化算法来找到最佳组合，以降低假体模拟器对侧肢体的加载率。为了评估优化算法的性能，我们使用了一个收敛标准。我们通过将其与所有组合的最佳扫描进行比较来评估准确性。结果：八名参与者中有五名，人在环优化减少了寻找最优组合所需的时间；然而，在三个参与者中，人在循环优化要么在最后一次迭代时收敛，要么不收敛。讨论：这项研究的结果表明，在需要手动调整辅助设备的任务中，人在环方法可能会有所帮助。比如优化无动力假肢。然而,需要进一步的研究来实现稳健的性能,并评估佩戴实际假肢的截肢者的适用性.
