Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees

In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize...

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Autores principales:	Jianyu Yang, Guanchao Li, Xiaofei Zhao, Hualong Xie
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	asymmetric lower extremity exoskeleton electromyographic signals artificial neural network joint-angle prediction going up and downstairs Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/6b29b93508214b259ff1d015e24db74f
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spelling	oai:doaj.org-article:6b29b93508214b259ff1d015e24db74f2021-11-11T19:11:20ZResearch on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees10.3390/s212171991424-8220https://doaj.org/article/6b29b93508214b259ff1d015e24db74f2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7199https://doaj.org/toc/1424-8220In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize the function of assisted walking when going up and downstairs. Currently, the use of lower-limb electromyography to predict the angles of lower limb joints has achieved remarkable results. However, the prediction effect was poor when only using electromyography from the thigh. Therefore, this paper introduces hip-angle and plantar pressure signals for improving prediction effect and puts forward a joint prediction method of knee- and ankle-joint angles by electromyography of the thigh, hip-joint angle, and plantar pressure signals. The generalized regression neural network optimized by the golden section method is used to predict the joint angles. Finally, the parameters (the maximum error, the Root-Mean-Square error (<i>RMSE</i>), and correlation coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula>)) were calculated to verify the feasibility of the prediction method.Jianyu YangGuanchao LiXiaofei ZhaoHualong XieMDPI AGarticleasymmetric lower extremity exoskeletonelectromyographic signalsartificial neural networkjoint-angle predictiongoing up and downstairsChemical technologyTP1-1185ENSensors, Vol 21, Iss 7199, p 7199 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	asymmetric lower extremity exoskeleton electromyographic signals artificial neural network joint-angle prediction going up and downstairs Chemical technology TP1-1185
spellingShingle	asymmetric lower extremity exoskeleton electromyographic signals artificial neural network joint-angle prediction going up and downstairs Chemical technology TP1-1185 Jianyu Yang Guanchao Li Xiaofei Zhao Hualong Xie Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees
description	In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize the function of assisted walking when going up and downstairs. Currently, the use of lower-limb electromyography to predict the angles of lower limb joints has achieved remarkable results. However, the prediction effect was poor when only using electromyography from the thigh. Therefore, this paper introduces hip-angle and plantar pressure signals for improving prediction effect and puts forward a joint prediction method of knee- and ankle-joint angles by electromyography of the thigh, hip-joint angle, and plantar pressure signals. The generalized regression neural network optimized by the golden section method is used to predict the joint angles. Finally, the parameters (the maximum error, the Root-Mean-Square error (<i>RMSE</i>), and correlation coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula>)) were calculated to verify the feasibility of the prediction method.
format	article
author	Jianyu Yang Guanchao Li Xiaofei Zhao Hualong Xie
author_facet	Jianyu Yang Guanchao Li Xiaofei Zhao Hualong Xie
author_sort	Jianyu Yang
title	Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees
title_short	Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees
title_full	Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees
title_fullStr	Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees
title_full_unstemmed	Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees
title_sort	research on joint-angle prediction based on artificial neural network for above-knee amputees
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6b29b93508214b259ff1d015e24db74f
work_keys_str_mv	AT jianyuyang researchonjointanglepredictionbasedonartificialneuralnetworkforabovekneeamputees AT guanchaoli researchonjointanglepredictionbasedonartificialneuralnetworkforabovekneeamputees AT xiaofeizhao researchonjointanglepredictionbasedonartificialneuralnetworkforabovekneeamputees AT hualongxie researchonjointanglepredictionbasedonartificialneuralnetworkforabovekneeamputees
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Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees

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