Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection
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...
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doaj-a83ade542147497fad38b96f15533c902021-07-15T15:45:15ZengMDPI AGSensors1424-82202021-06-01214372437210.3390/s21134372Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort DetectionJenny Carolina Castiblanco0Ivan Fernando Mondragon1Catalina Alvarado-Rojas2Julian D. Colorado3School of Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110231, ColombiaDepartment of Industrial Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110231, ColombiaDepartment of Electronics Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110231, ColombiaDepartment of Electronics Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110231, ColombiaRobotic-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%.https://www.mdpi.com/1424-8220/21/13/4372active controlrobotic-assisted systemsEMG controlstroke rehabilitationhand motion rehabilitationhand exoskeleton orthosis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jenny Carolina Castiblanco Ivan Fernando Mondragon Catalina Alvarado-Rojas Julian D. Colorado |
spellingShingle |
Jenny Carolina Castiblanco Ivan Fernando Mondragon Catalina Alvarado-Rojas Julian D. Colorado Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection Sensors active control robotic-assisted systems EMG control stroke rehabilitation hand motion rehabilitation hand exoskeleton orthosis |
author_facet |
Jenny Carolina Castiblanco Ivan Fernando Mondragon Catalina Alvarado-Rojas Julian D. Colorado |
author_sort |
Jenny Carolina Castiblanco |
title |
Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection |
title_short |
Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection |
title_full |
Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection |
title_fullStr |
Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection |
title_full_unstemmed |
Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection |
title_sort |
assist-as-needed exoskeleton for hand joint rehabilitation based on muscle effort detection |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-06-01 |
description |
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%. |
topic |
active control robotic-assisted systems EMG control stroke rehabilitation hand motion rehabilitation hand exoskeleton orthosis |
url |
https://www.mdpi.com/1424-8220/21/13/4372 |
work_keys_str_mv |
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