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Wheelchair-mounted upper limb robotic exoskeleton with adaptive controller for activities of daily living
Schabron, Bridget Desai, Jaydip M. Yihun, Yimesker S.
Schabron, Bridget
Desai, Jaydip M.
Yihun, Yimesker S.
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2021-08-26
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Article
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Keywords
Electromyography,Artificial neural network,Exoskeleton,Assistive technology,Robotics,Hand gestures
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Citation
Schabron, B., Desai, J., & Yihun, Y. (2021). Wheelchair-mounted upper limb robotic exoskeleton with adaptive controller for activities of daily living. Sensors, 21(17) doi:10.3390/s21175738
Abstract
Neuro-muscular disorders and diseases such as cerebral palsy and Duchenne Muscular
Dystrophy can severely limit a person’s ability to perform activities of daily living (ADL). Exoskeletons can provide an active or passive support solution to assist these groups of people to perform
ADL. This study presents an artificial neural network-trained adaptive controller mechanism that
uses surface electromyography (sEMG) signals from the human forearm to detect hand gestures and
navigate an in-house-built wheelchair-mounted upper limb robotic exoskeleton based on the user’s
intent while ensuring safety. To achieve the desired position of the exoskeleton based on human
intent, 10 hand gestures were recorded from 8 participants without upper limb movement disabilities.
Participants were tasked to perform water bottle pick and place activities while using the exoskeleton,
and sEMG signals were collected from the forearm and processed through root mean square, median
filter, and mean feature extractors prior to training a scaled conjugate gradient backpropagation
artificial neural network. The trained network achieved an average of more than 93% accuracy, while
all 8 participants who did not have any prior experience of using an exoskeleton were successfully
able to perform the task in less than 20 s using the proposed artificial neural network-trained adaptive
controller mechanism. These results are significant and promising thus could be tested on people
with muscular dystrophy and neuro-degenerative diseases.
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Publisher
MDPI
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Series
Sensors;Vol. 21, Iss. 17
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1424-8220