Joint angle estimation with wavelet neural networks

Abstract This paper presents a wavelet neural network (WNN) based method to reduce reliance on wearable kinematic sensors in gait analysis. Wearable kinematic sensors hinder real-time outdoor gait monitoring applications due to drawbacks caused by multiple sensor placements and sensor offset errors....

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Autores principales:	Saaveethya Sivakumar, Alpha Agape Gopalai, King Hann Lim, Darwin Gouwanda, Sunita Chauhan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b27142550d014fc5a411dcfbbe988761
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spelling	oai:doaj.org-article:b27142550d014fc5a411dcfbbe9887612021-12-02T16:50:27ZJoint angle estimation with wavelet neural networks10.1038/s41598-021-89580-y2045-2322https://doaj.org/article/b27142550d014fc5a411dcfbbe9887612021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89580-yhttps://doaj.org/toc/2045-2322Abstract This paper presents a wavelet neural network (WNN) based method to reduce reliance on wearable kinematic sensors in gait analysis. Wearable kinematic sensors hinder real-time outdoor gait monitoring applications due to drawbacks caused by multiple sensor placements and sensor offset errors. The proposed WNN method uses vertical Ground Reaction Forces (vGRFs) measured from foot kinetic sensors as inputs to estimate ankle, knee, and hip joint angles. Salient vGRF inputs are extracted from primary gait event intervals. These selected gait inputs facilitate future integration with smart insoles for real-time outdoor gait studies. The proposed concept potentially reduces the number of body-mounted kinematics sensors used in gait analysis applications, hence leading to a simplified sensor placement and control circuitry without deteriorating the overall performance.Saaveethya SivakumarAlpha Agape GopalaiKing Hann LimDarwin GouwandaSunita ChauhanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Saaveethya Sivakumar Alpha Agape Gopalai King Hann Lim Darwin Gouwanda Sunita Chauhan Joint angle estimation with wavelet neural networks
description	Abstract This paper presents a wavelet neural network (WNN) based method to reduce reliance on wearable kinematic sensors in gait analysis. Wearable kinematic sensors hinder real-time outdoor gait monitoring applications due to drawbacks caused by multiple sensor placements and sensor offset errors. The proposed WNN method uses vertical Ground Reaction Forces (vGRFs) measured from foot kinetic sensors as inputs to estimate ankle, knee, and hip joint angles. Salient vGRF inputs are extracted from primary gait event intervals. These selected gait inputs facilitate future integration with smart insoles for real-time outdoor gait studies. The proposed concept potentially reduces the number of body-mounted kinematics sensors used in gait analysis applications, hence leading to a simplified sensor placement and control circuitry without deteriorating the overall performance.
format	article
author	Saaveethya Sivakumar Alpha Agape Gopalai King Hann Lim Darwin Gouwanda Sunita Chauhan
author_facet	Saaveethya Sivakumar Alpha Agape Gopalai King Hann Lim Darwin Gouwanda Sunita Chauhan
author_sort	Saaveethya Sivakumar
title	Joint angle estimation with wavelet neural networks
title_short	Joint angle estimation with wavelet neural networks
title_full	Joint angle estimation with wavelet neural networks
title_fullStr	Joint angle estimation with wavelet neural networks
title_full_unstemmed	Joint angle estimation with wavelet neural networks
title_sort	joint angle estimation with wavelet neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b27142550d014fc5a411dcfbbe988761
work_keys_str_mv	AT saaveethyasivakumar jointangleestimationwithwaveletneuralnetworks AT alphaagapegopalai jointangleestimationwithwaveletneuralnetworks AT kinghannlim jointangleestimationwithwaveletneuralnetworks AT darwingouwanda jointangleestimationwithwaveletneuralnetworks AT sunitachauhan jointangleestimationwithwaveletneuralnetworks
_version_	1718383058981224448

Joint angle estimation with wavelet neural networks

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