Wearable motion sensors and artificial neural network for the estimation of vertical ground reaction forces in running

Simple item page
dc.contributor.authorTedesco, Salvatore
dc.contributor.authorPerez-Valero, Eduardo
dc.contributor.authorKomaris, Dimitrios-Sokratis
dc.contributor.authorJordan, Luke
dc.contributor.authorBarton, John
dc.contributor.authorHennessy, Liam
dc.contributor.authorO'Flynn, Brendan
dc.contributor.funderEnterprise Irelanden
dc.contributor.funderSetanta College Ltd., Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2021-02-09T16:07:57Z
dc.date.available2021-02-09T16:07:57Z
dc.date.issued2020-10
dc.date.updated2021-02-09T15:58:29Z
dc.description.abstractBiomechanical load assessments are becoming increasingly important in the sporting community; however, there are still numerous difficulties in monitoring them in a field environment outside of specialized biomechanical monitoring laboratories. Inertial Measurements Units (IMUs) have been showing promising results in the modeling of biomechanical variables. This study explores the application of an artificial neural network (ANN) in the estimation of runners' vertical ground reaction forces (GRFs) based on the accelerometry collected from two wearable motion sensors developed in-house and attached on the shanks. Data collected from fourteen runners running at three different speeds (8, 10, 12 km/h) were used to train and validate the ANN. Predictions were compared against gold-standard measurements from a pair of pressure in-soles. Root mean square error (RMSE) was used to evaluate the performance of the models. Further investigations, e.g., the use of principal components analysis (PCA) and the impact on the estimation of several GRF-related variables, were carried out to provide useful insights regarding the portability of the model to low-power resource-constrained devices. Findings indicate that ANNs in conjunction with accelerometry may be used to compute vertical ground reaction forces (RMSE: 0.148 BW) and related loading metrics in running accurately.en
dc.description.sponsorshipEnterprise Ireland and Setanta College Ltd (under grant agreement no. IP 2017 0606); European Regional Development Fund (ERDF under Ireland’s European Structural and Investment Funds Programmes 2014-2020); Science Foundation Ireland (under Grant number 12/RC/2289-P2 INSIGHT-2 which is co-funded under the ERDF)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationTedesco, S., Perez-Valero, E., Komaris, D. S., Jordan, L., Barton, J., Hennessy, L. and O’Flynn, B. (2020) 'Wearable motion sensors and artificial neural network for the estimation of vertical ground reaction forces in running', 2020 IEEE SENSORS, Rotterdam, Netherlands, 25-28 Oct. doi: 10.1109/SENSORS47125.2020.9278796en
dc.identifier.doi10.1109/SENSORS47125.2020.9278796en
dc.identifier.eissn2168-9229
dc.identifier.endpage4en
dc.identifier.isbn978-1-7281-6801-2
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/11055
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers, IEEEen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://ieeexplore.ieee.org/document/9278796
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectAccelerometersen
dc.subjectGround Reaction Forcesen
dc.subjectIMUen
dc.subjectRunningen
dc.subjectWearableen
dc.subjectPrincipal component analysisen
dc.subjectNeuronsen
dc.subjectEstimationen
dc.subjectArtificial neural networksen
dc.subjectTrainingen
dc.subjectLoad modelingen
dc.subjectMeasurementen
dc.titleWearable motion sensors and artificial neural network for the estimation of vertical ground reaction forces in runningen
dc.typeConference itemen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
PID6598227.pdf
Size:
1.41 MB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Tyndall National Institute - Conference Items
Insight Centre for Data Analytics - Conference Items