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Optimizing Human–Exoskeleton Physical Interaction Through Spatial Trajectory Adaptation

delete2026-02-11
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PRE
AI
M
Mohammad Shushtari
L
Livia Murray
A
Atusa Ghorbani Siavashani
A
Arash Arami
DOI:10.1109/TRO.2026.3663979delete
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Abstract

Abstract

En 中文
This study presents and experimentally validates an adaptive control method for human-exoskeleton interaction through online adaptation of desired joint trajectories. Leveraging gait phase and human-exoskeleton interaction torque estimators, our approach enables seamless assistance adaptation to varying walking patterns and speeds. Specifically, a pretrained neural network approximates the exoskeleton’s dynamics, enabling real-time interaction torque estimation from kinematic measurements and commanded motor torques alone. These estimates drive a gradient-descent update of the joint reference trajectories, minimizing a cost function that penalizes both interaction torques and trajectory modification, ensuring bounded convergence and stability without user-specific parameter tuning. We compared our adaptive controller with a fixed-trajectory gait-phase-based controller during overground and treadmill walking at three self-selected speeds ranging from 0.4 to 0.8 m/s. In 16 participants, the adaptive controller significantly reduced the hip and knee interaction torques by 51.2% $\pm$11.1 and 63.9% $\pm$29.7, respectively, during overground walking. Muscular effort significantly decreased in Bicep Femoris (21.0% $\pm$34.5) and Rectus Femoris (28.1% $\pm$34.6), while remaining unchanged in other muscles. Cadence and gait speed increased by 7.6% $\pm$5.2 and 10.7% $\pm$8.3, respectively, indicating that participants could walk faster with less effort due to trajectory adaptation. Postadaptation trajectories more closely resembled those of walking without the exoskeleton, and exoskeleton-torques aligned more closely with human biological torques. Our proposed adaptive controller, which requires only exoskeleton kinematics, also maintained performance during treadmill walking across speeds, demonstrating speed-invariant behavior compared to the nonadaptive controller.
Keywords:
Human-exoskeleton physical interaction
rehabilitation
trajectory adaptation

Journal

IEEE Transactions on Robotics cover
IEEE Transactions on Robotics
IF:
10.5
Papers:
3.3K
Citations:
2.8W

Organization

U
university of waterloo
Scholars:
2.4K
Papers: 1.3K
Citations: 1