Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump

Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint mom...

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Autores principales:	Serena Cerfoglio, Manuela Galli, Marco Tarabini, Filippo Bertozzi, Chiarella Sforza, Matteo Zago
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	vertical drop jump wearable sensors neural networks Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/f287c34a09484ee6918a1ee6d422b599
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spelling	oai:doaj.org-article:f287c34a09484ee6918a1ee6d422b5992021-11-25T18:58:36ZMachine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump10.3390/s212277091424-8220https://doaj.org/article/f287c34a09484ee6918a1ee6d422b5992021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7709https://doaj.org/toc/1424-8220Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method.Serena CerfoglioManuela GalliMarco TarabiniFilippo BertozziChiarella SforzaMatteo ZagoMDPI AGarticlevertical drop jumpwearable sensorsneural networksChemical technologyTP1-1185ENSensors, Vol 21, Iss 7709, p 7709 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	vertical drop jump wearable sensors neural networks Chemical technology TP1-1185
spellingShingle	vertical drop jump wearable sensors neural networks Chemical technology TP1-1185 Serena Cerfoglio Manuela Galli Marco Tarabini Filippo Bertozzi Chiarella Sforza Matteo Zago Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump
description	Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method.
format	article
author	Serena Cerfoglio Manuela Galli Marco Tarabini Filippo Bertozzi Chiarella Sforza Matteo Zago
author_facet	Serena Cerfoglio Manuela Galli Marco Tarabini Filippo Bertozzi Chiarella Sforza Matteo Zago
author_sort	Serena Cerfoglio
title	Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump
title_short	Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump
title_full	Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump
title_fullStr	Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump
title_full_unstemmed	Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump
title_sort	machine learning-based estimation of ground reaction forces and knee joint kinetics from inertial sensors while performing a vertical drop jump
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f287c34a09484ee6918a1ee6d422b599
work_keys_str_mv	AT serenacerfoglio machinelearningbasedestimationofgroundreactionforcesandkneejointkineticsfrominertialsensorswhileperformingaverticaldropjump AT manuelagalli machinelearningbasedestimationofgroundreactionforcesandkneejointkineticsfrominertialsensorswhileperformingaverticaldropjump AT marcotarabini machinelearningbasedestimationofgroundreactionforcesandkneejointkineticsfrominertialsensorswhileperformingaverticaldropjump AT filippobertozzi machinelearningbasedestimationofgroundreactionforcesandkneejointkineticsfrominertialsensorswhileperformingaverticaldropjump AT chiarellasforza machinelearningbasedestimationofgroundreactionforcesandkneejointkineticsfrominertialsensorswhileperformingaverticaldropjump AT matteozago machinelearningbasedestimationofgroundreactionforcesandkneejointkineticsfrominertialsensorswhileperformingaverticaldropjump
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Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump

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