Abstract
Robotic-assisted systems have gained significant traction in post-stroke therapies to support rehabilitation, since these systems can provide high-intensity and high-frequency treatment while allowing accurate motion-control over the patient’s progress. In this paper, we tackle how to provide active support through a robotic-assisted exoskeleton by developing a novel closed-loop architecture that continually measures electromyographic signals (EMG), in order to adjust the assistance given by the exoskeleton. We used EMG signals acquired from four patients with post-stroke hand impairments for training machine learning models used to characterize muscle effort by classifying three muscular condition levels based on contraction strength, co-activation, and muscular activation measurements. The proposed closed-loop system takes into account the EMG muscle effort to modulate the exoskeleton velocity during the rehabilitation therapy. Experimental results indicate the maximum variation on velocity was 0.7 mm/s, while the proposed control system effectively modulated the movements of the exoskeleton based on the EMG readings, keeping a reference tracking error <5%.
Highlights
Most of the body of work is oriented at characterizing muscle effort based on measuring muscle force from electromyographic signals (EMG) signals [23,24,25], we found that EMG signals are acquired from agonist and antagonist muscle groups, restricting the degree of freedom of the system for more than two motions
The results show that the velocity value corresponds to the fuzzy rules expected, even if the artificial neural networks (ANNs) outputs present an error
This paper was focused on the design and implementation of an assist-as-needed robotic exoskeleton for hand rehabilitation
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Robotic-assisted systems have gained significant interest in movement rehabilitation in the last decade [1,2,3]. When motor disabilities are generated by a stroke event, roboticassisted systems can significantly improve the intensity and frequency of the treatment. They can speed up rehabilitation progress by increasing accurate motion control by enabling continuous monitoring during the therapies [4,5]
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