Understanding the effects of different hip assistance modes is a fundamental step in the process of designing hip assistance devices and controllers that can provide better performance in terms of metabolic cost. We have developed and tested a soft exoskeleton for hip assistance, which includes three assistance modes: hip extension assistance (HEA), hip flexion assistance (HFA), and hip extension and flexion assistance (HEFA). A proportional derivative (PD) iterative learning controller based on the feedforward model was proposed to control the assistive force accurately. The three hip assistance modes were evaluated on seven male subjects walking on a treadmill at a speed of 5 km/h in two scenarios-first with a 15-kg backpack and then without any backpack. The net metabolic costs could be reduced during the loaded condition, compared with those under no exoskeleton condition, by 9.95%, 6.25%, and 15.28% for HEA, HFA, and HEFA, respectively. The reductions were found significant in HEA ( p=0.048) and HEFA ( p=0.005) modes, while the HFA mode ( p=0.202) was not found statistically significant. It indicates that the HEA and HEFA modes with the soft exoskeleton provide more benefit to the net metabolic cost compared with the HFA mode. The net metabolic costs reduced during the unloaded condition were 9.21%, 2.58%, and 13.05% for HEA, HFA, and HEFA, respectively. The improvements in the walking efficiency during both the conditions with the developed soft exoskeleton are demonstrated. Note to Practitioners-This article was motivated by the problem that how to reduce the metabolic cost most appropriately of walking by hip assistance of soft exoskeleton. In this article, we conduct a comparison of three hip assistance modes to discuss the balance of system weight and assistance efficiency. We then propose a PD iterative learning controller based on the feedforward model to track the desired assistive force accurately. Preliminary experiments suggest that the hip extension assistance (HEA) is more suitable than hip flexion assistance (HFA) for hip assistance during single motion assistance. Multiple motion assistance is more beneficial for metabolic cost reduction when the weight of the soft exoskeleton is the same. The experimental tests show that the proposed soft exoskeleton and the control algorithm are effective for walking assistance during the loaded condition. However, the performance of the hip assistance device is tested based on the treadmill walking only. In future research, we will conduct a performance evaluation of the soft exoskeleton on a complex road environment.

中文翻译：

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髋关节辅助模式对软外骨骼行走代谢成本的影响


了解不同髋关节辅助模式的效果是设计髋关节辅助设备和控制器过程中的一个基本步骤，这些设备和控制器可以在代谢成本方面提供更好的性能。我们开发并测试了一种用于髋部辅助的软外骨骼，包括三种辅助模式：髋部伸展辅助（HEA）、髋部屈曲辅助（HFA）、髋部伸展和屈曲辅助（HEFA）。提出一种基于前馈模型的比例微分（PD）迭代学习控制器来精确控制辅助力。对 7 名男性受试者在两种情况下以 5 公里/小时的速度在跑步机上行走的三种髋部辅助模式进行了评估：首先背着 15 公斤的背包，然后不带任何背包。与无外骨骼条件下相比，在负载条件下，HEA、HFA 和 HEFA 的净代谢成本可分别降低 9.95%、6.25% 和 15.28%。在 HEA (p=0.048) 和 HEFA (p=0.005) 模式中发现显着降低，而 HFA 模式 (p=0.202) 则没有发现统计显着性。这表明与 HFA 模式相比，具有软外骨骼的 HEA 和 HEFA 模式对净代谢成本有更多好处。 HEA、HFA 和 HEFA 在卸载条件下的净代谢成本降低分别为 9.21%、2.58% 和 13.05%。证明了开发的软外骨骼在这两种条件下步行效率的提高。从业者须知——本文的出发点是如何通过软外骨骼的髋部辅助来最适当地降低行走的代谢成本。 在本文中，我们对三种髋部辅助模式进行比较，以讨论系统重量和辅助效率的平衡。然后，我们提出了一种基于前馈模型的 PD 迭代学习控制器，以准确跟踪所需的辅助力。初步实验表明，髋部伸展辅助（HEA）比髋部屈曲辅助（HFA）更适合单次运动辅助时的髋部辅助。在软外骨骼重量相同的情况下，多种运动辅助更有利于降低代谢成本。实验测试表明，所提出的软外骨骼和控制算法对于负载条件下的步行辅助是有效的。然而，臀部辅助装置的性能仅基于跑步机行走进行测试。在未来的研究中，我们将对软外骨骼在复杂道路环境下的性能进行评估。