Estimating human joint moments unifies exoskeleton control and reduces user effort

Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance based on instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average RMSE of 0.142 ± 0.021 Nm/kg and R2 of 0.840 ± 0.045 across 35 ambulatory conditions without any subject-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level ground and incline walking compared to walking without wearing the exoskeleton (P < 0.05). This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.

下肢外骨骼机器人可增强人类移动能力，但现有外骨骼系统需开展大量场景定制化适配工作，限制了其实际落地可行性。本研究提出一种统一的外骨骼控制框架，可基于时序卷积网络（Temporal Convolutional Network，TCN）对用户关节力矩的实时估计结果，自主调节辅助输出强度。将该框架部署于自研髋关节外骨骼平台后，TCN在35种行走场景下均实现了平均均方根误差（Root Mean Square Error，RMSE）0.142 ± 0.021 Nm/kg、平均决定系数（Coefficient of Determination，R²）0.840 ± 0.045的预测性能，且无需针对受试者进行个性化校准。此外，相较于未穿戴外骨骼的自然行走状态，该统一控制器可显著降低使用者的代谢能耗与下肢正向做功，在平地行走与上坡行走场景下均达到统计学显著性差异（P < 0.05）。该研究成果填补了实验室外骨骼技术与实际人体行走应用之间的技术鸿沟，使得外骨骼控制技术能够面向更广泛的人群实现落地应用。