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.

搜集汇总

背景与挑战

背景概述

该数据集包含机器人髋部外骨骼传感器数据与地面真实人体下肢生物力学数据的同步记录，采样频率为200 Hz，涉及34名参与者在多种运动模式下的实验。数据集支持一个基于时间卷积网络的统一外骨骼控制框架，该框架通过估计关节力矩来自主适应辅助，旨在减少用户行走时的代谢成本和下肢正功，从而提升外骨骼在现实世界中的适用性。