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Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation

Stroke and related complications such as hemiplegia and disability create huge burdens for human society in the 21st century, which leads to a great need for rehabilitation and daily life assistance. To address this issue, continuous efforts are devoted in human–machine interaction (HMI) technology,...

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Autores principales: Xia, Kang, Chen, Xianglei, Chang, Xuedong, Liu, Chongshuai, Guo, Liwei, Xu, Xiaobin, Lv, Fangrui, Wang, Yimin, Sun, Han, Zhou, Jianfang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687420/
https://www.ncbi.nlm.nih.gov/pubmed/36421083
http://dx.doi.org/10.3390/bioengineering9110682
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author Xia, Kang
Chen, Xianglei
Chang, Xuedong
Liu, Chongshuai
Guo, Liwei
Xu, Xiaobin
Lv, Fangrui
Wang, Yimin
Sun, Han
Zhou, Jianfang
author_facet Xia, Kang
Chen, Xianglei
Chang, Xuedong
Liu, Chongshuai
Guo, Liwei
Xu, Xiaobin
Lv, Fangrui
Wang, Yimin
Sun, Han
Zhou, Jianfang
author_sort Xia, Kang
collection PubMed
description Stroke and related complications such as hemiplegia and disability create huge burdens for human society in the 21st century, which leads to a great need for rehabilitation and daily life assistance. To address this issue, continuous efforts are devoted in human–machine interaction (HMI) technology, which aims to capture and recognize users’ intentions and fulfil their needs via physical response. Based on the physiological structure of the human hand, a dimension-adjustable linkage-driven hand exoskeleton with 10 active degrees of freedom (DoFs) and 3 passive DoFs is proposed in this study, which grants high-level synergy with the human hand. Considering the weight of the adopted linkage design, the hand exoskeleton can be mounted on the existing up-limb exoskeleton system, which greatly diminishes the burden for users. Three rehabilitation/daily life assistance modes are developed (namely, robot-in-charge, therapist-in-charge, and patient-in-charge modes) to meet specific personal needs. To realize HMI, a thin-film force sensor matrix and Inertial Measurement Units (IMUs) are installed in both the hand exoskeleton and the corresponding controller. Outstanding sensor–machine synergy is confirmed by trigger rate evaluation, Kernel Density Estimation (KDE), and a confusion matrix. To recognize user intention, a genetic algorithm (GA) is applied to search for the optimal hyperparameters of a 1D Convolutional Neural Network (CNN), and the average intention-recognition accuracy for the eight actions/gestures examined reaches 97.1% (based on K-fold cross-validation). The hand exoskeleton system provides the possibility for people with limited exercise ability to conduct self-rehabilitation and complex daily activities.
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spelling pubmed-96874202022-11-25 Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation Xia, Kang Chen, Xianglei Chang, Xuedong Liu, Chongshuai Guo, Liwei Xu, Xiaobin Lv, Fangrui Wang, Yimin Sun, Han Zhou, Jianfang Bioengineering (Basel) Article Stroke and related complications such as hemiplegia and disability create huge burdens for human society in the 21st century, which leads to a great need for rehabilitation and daily life assistance. To address this issue, continuous efforts are devoted in human–machine interaction (HMI) technology, which aims to capture and recognize users’ intentions and fulfil their needs via physical response. Based on the physiological structure of the human hand, a dimension-adjustable linkage-driven hand exoskeleton with 10 active degrees of freedom (DoFs) and 3 passive DoFs is proposed in this study, which grants high-level synergy with the human hand. Considering the weight of the adopted linkage design, the hand exoskeleton can be mounted on the existing up-limb exoskeleton system, which greatly diminishes the burden for users. Three rehabilitation/daily life assistance modes are developed (namely, robot-in-charge, therapist-in-charge, and patient-in-charge modes) to meet specific personal needs. To realize HMI, a thin-film force sensor matrix and Inertial Measurement Units (IMUs) are installed in both the hand exoskeleton and the corresponding controller. Outstanding sensor–machine synergy is confirmed by trigger rate evaluation, Kernel Density Estimation (KDE), and a confusion matrix. To recognize user intention, a genetic algorithm (GA) is applied to search for the optimal hyperparameters of a 1D Convolutional Neural Network (CNN), and the average intention-recognition accuracy for the eight actions/gestures examined reaches 97.1% (based on K-fold cross-validation). The hand exoskeleton system provides the possibility for people with limited exercise ability to conduct self-rehabilitation and complex daily activities. MDPI 2022-11-11 /pmc/articles/PMC9687420/ /pubmed/36421083 http://dx.doi.org/10.3390/bioengineering9110682 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Xia, Kang
Chen, Xianglei
Chang, Xuedong
Liu, Chongshuai
Guo, Liwei
Xu, Xiaobin
Lv, Fangrui
Wang, Yimin
Sun, Han
Zhou, Jianfang
Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation
title Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation
title_full Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation
title_fullStr Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation
title_full_unstemmed Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation
title_short Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation
title_sort hand exoskeleton design and human–machine interaction strategies for rehabilitation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687420/
https://www.ncbi.nlm.nih.gov/pubmed/36421083
http://dx.doi.org/10.3390/bioengineering9110682
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