Human-in-the-Loop Weight Compensation in Upper Limb Wearable Robots Towards Total Muscles’ Effort Minimization

Nasiri, Rezvan; Aftabi, Hamidreza; Rayati, Mojtaba; Ahmadabadi, Majid Nili

doi:10.1101/2020.11.02.366070

Cited by 1 publication

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References 43 publications

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“…For instance, Nasiri et al [28] introduced a general adaptation rule to optimize the robotic assistive torque which minimizes the actuator torque. This adaptation rule is extended for wearable robots in [29,30] and its generality for lower limb exoskeletons is studied in [31]. However, in SCI individuals, the muscles' EMG signals are not sufficiently reliable for controller adaptation.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation

2021

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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.

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