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Communication

An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation

1
Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
2
Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
3
Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Dan Zhang and Kean C. Aw
Robotics 2021, 10(3), 95; https://doi.org/10.3390/robotics10030095
Received: 1 June 2021 / Revised: 9 July 2021 / Accepted: 21 July 2021 / Published: 24 July 2021
(This article belongs to the Section Medical Robotics and Service Robotics)
In this paper, we present a novel adaptation rule to optimize the exoskeleton assistance in rehabilitation tasks. The proposed method adapts the exoskeleton contribution to user impairment severity without any prior knowledge about the user motor capacity. The proposed controller is a combination of an adaptive feedforward controller and a low gain adaptive PD controller. The PD controller guarantees the stability of the human-exoskeleton system during feedforward torque adaptation by utilizing only the human-exoskeleton joint positions as the sensory feedback for assistive torque optimization. In addition to providing a convergence proof, in order to study the performance of our method we applied it to a simplified 2-DOF model of human-arm and a generic 9-DOF model of lower limb to perform walking. In each simulated task, we implemented the impaired human torque to be insufficient for the task completion. Moreover, the scenarios that violate our convergence proof assumptions are considered. The simulation results show a converging behavior for the proposed controller; the maximum convergence time of 20 s is observed. In addition, a stable control performance that optimally supplements the remaining user motor contribution is observed; the joint angle tracking error in steady condition and its improvement compared to the start of adaptation are as follows: shoulder 0.96±2.53° (76%); elbow 0.35±0.81° (33%); hip 0.10±0.86° (38%); knee 0.19±0.67° (25%); and ankle 0.05±0.20° (60%). The presented simulation results verify the robustness of proposed adaptive method in cases that differ from our mathematical assumptions and indicate its potentials to be used in practice. View Full-Text
Keywords: rehabilitation robotics; exoskeleton adaptation; optimal assistance; assist-as-needed; feedforward control rehabilitation robotics; exoskeleton adaptation; optimal assistance; assist-as-needed; feedforward control
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MDPI and ACS Style

Nasiri, R.; Shushtari, M.; Arami, A. An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation. Robotics 2021, 10, 95. https://doi.org/10.3390/robotics10030095

AMA Style

Nasiri R, Shushtari M, Arami A. An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation. Robotics. 2021; 10(3):95. https://doi.org/10.3390/robotics10030095

Chicago/Turabian Style

Nasiri, Rezvan, Mohammad Shushtari, and Arash Arami. 2021. "An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation" Robotics 10, no. 3: 95. https://doi.org/10.3390/robotics10030095

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