A Novel Foot-Forward Segmentation Algorithm for Improving IMU-based Gait Analysis

The use of gait analysis techniques to identify health conditions has increased significantly in recent years. Among the many technologies available, inertial sensor-based solutions are one of the most popular due to their cost, accuracy, and portability. However, the accuracy obtained in gait analy...

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Authors: Ruiz-Ruiz, Luisa, García-Domínguez, Juan Jesús, Jiménez Ruiz, Antonio R.
Format: article
Status:Published version
Publication Date:2024
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/374515
Online Access:http://hdl.handle.net/10261/374515
Access Level:Open access
Keyword:EKF
gait segmentation
inertial measurement unit (IMU)
INS-ZUPT
motion analysis.
id ES_9eeca3e2991da2956f87f24a6a12bb08
oai_identifier_str oai:digital.csic.es:10261/374515
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv A Novel Foot-Forward Segmentation Algorithm for Improving IMU-based Gait Analysis
title A Novel Foot-Forward Segmentation Algorithm for Improving IMU-based Gait Analysis
spellingShingle A Novel Foot-Forward Segmentation Algorithm for Improving IMU-based Gait Analysis
Ruiz-Ruiz, Luisa
EKF
gait segmentation
inertial measurement unit (IMU)
INS-ZUPT
motion analysis.
title_short A Novel Foot-Forward Segmentation Algorithm for Improving IMU-based Gait Analysis
title_full A Novel Foot-Forward Segmentation Algorithm for Improving IMU-based Gait Analysis
title_fullStr A Novel Foot-Forward Segmentation Algorithm for Improving IMU-based Gait Analysis
title_full_unstemmed A Novel Foot-Forward Segmentation Algorithm for Improving IMU-based Gait Analysis
title_sort A Novel Foot-Forward Segmentation Algorithm for Improving IMU-based Gait Analysis
dc.creator.none.fl_str_mv Ruiz-Ruiz, Luisa
García-Domínguez, Juan Jesús
Jiménez Ruiz, Antonio R.
author Ruiz-Ruiz, Luisa
author_facet Ruiz-Ruiz, Luisa
García-Domínguez, Juan Jesús
Jiménez Ruiz, Antonio R.
author_role author
author2 García-Domínguez, Juan Jesús
Jiménez Ruiz, Antonio R.
author2_role author
author
dc.contributor.none.fl_str_mv European Commission
Ministerio de Ciencia e Innovación (España)
Ruiz-Ruiz, Luisa [0000-0003-0316-7781]
García-Domínguez, Juan Jesús [0000-0002-7121-8651]
Jiménez Ruiz, Antonio R. [0000-0001-9771-1930]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv EKF
gait segmentation
inertial measurement unit (IMU)
INS-ZUPT
motion analysis.
topic EKF
gait segmentation
inertial measurement unit (IMU)
INS-ZUPT
motion analysis.
description The use of gait analysis techniques to identify health conditions has increased significantly in recent years. Among the many technologies available, inertial sensor-based solutions are one of the most popular due to their cost, accuracy, and portability. However, the accuracy obtained in gait analysis is primarily determined by a reliable segmentation of the steps. Most classical algorithms use some direct information extracted from accelerometers and gyroscopes, such as angles or signal magnitudes, and although they are effective for standard walking modes, they are very sensitive to unusual walking styles. In this sense, it would be desirable to obtain an effective and robust walking-style segmentation method. In this article, we analyze and compare a typical angular velocity-based algorithm for gait segmentation (AVGS), a commercial software for gait analysis (GaitUpLab), and a new algorithm proposed by the authors based on foot-forward displacement for gait segmentation (FoDiGS). The three algorithms have been evaluated under different walking-style tests using the Optitrack optical system (gold stan dard). The new proposed FoDiGS algorithm detects 96.9% of the 3205 steps analyzed and improves the gait parameter estimation, decreasing the mean relative error (MRE) by 20.89% against the AVGS algorithm and by 28.87% compared with the commercial system GaitUp. The results suggest that the proposed method, which does not require any previous training with a database or adaptive thresholds, provides an accurate segmentation method for different walking modes and outperforms other well-known methods.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/374515
url http://hdl.handle.net/10261/374515
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122642OB-C43
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PCI2020-112040
Ruiz-Ruiz, Luisa; Neira Álvarez, Marta; Huertas Hoyas, Elisabet; Curiel-Regueros, Agustín; García, Rafael; Alonso-Bouzon, Cristina; García de Villa, Sara; Pilla Barroso, Melisa Janela; Seco Granja, Fernando; Jiménez Ruiz, Antonio R.; 2025; GAIT2CARE: A Database for Evaluating the Effectiveness of Two Exercise Programs in Older Adults using Inertial Gait Analysis and Functional Assessments [Dataset]; Zenodo; Version v1; https://doi.org/10.5281/zenodo.15276116
http://dx.doi.org/10.1109/TIM.2024.3449951
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling A Novel Foot-Forward Segmentation Algorithm for Improving IMU-based Gait AnalysisRuiz-Ruiz, LuisaGarcía-Domínguez, Juan JesúsJiménez Ruiz, Antonio R.EKFgait segmentationinertial measurement unit (IMU)INS-ZUPTmotion analysis.The use of gait analysis techniques to identify health conditions has increased significantly in recent years. Among the many technologies available, inertial sensor-based solutions are one of the most popular due to their cost, accuracy, and portability. However, the accuracy obtained in gait analysis is primarily determined by a reliable segmentation of the steps. Most classical algorithms use some direct information extracted from accelerometers and gyroscopes, such as angles or signal magnitudes, and although they are effective for standard walking modes, they are very sensitive to unusual walking styles. In this sense, it would be desirable to obtain an effective and robust walking-style segmentation method. In this article, we analyze and compare a typical angular velocity-based algorithm for gait segmentation (AVGS), a commercial software for gait analysis (GaitUpLab), and a new algorithm proposed by the authors based on foot-forward displacement for gait segmentation (FoDiGS). The three algorithms have been evaluated under different walking-style tests using the Optitrack optical system (gold stan dard). The new proposed FoDiGS algorithm detects 96.9% of the 3205 steps analyzed and improves the gait parameter estimation, decreasing the mean relative error (MRE) by 20.89% against the AVGS algorithm and by 28.87% compared with the commercial system GaitUp. The results suggest that the proposed method, which does not require any previous training with a database or adaptive thresholds, provides an accurate segmentation method for different walking modes and outperforms other well-known methods.This work was supported in part by the GAIT2CARE NextGeneration EU/PRTR Project (TED2021-132429B-I00) by MCIN/AEI/10.13039/501100011033; in part by the INDRI Projects (PID2021-122642OB-C43;-C41/AEI/10.13039/501100011033/FEDER, UE); in part by the NEXTPERCEPTION European Union Project funded by Electronic Components and Systems for European Leadership (ECSEL) Joint Undertaking (JU), which receives support from the European Union’s Horizon 2020 Research and Innovation Program and Finland, Spain (MCIN/AEI PCI2020-112040), Italy, Germany, Czech Republic, Belgium, and The Netherlands, under Grant 876487 (ECSEL-2019-2-RIA); and in part by the Junta de Comunidades de Castilla La Mancha and Union Europea through the “Fondo Europeo Desarrollo Regional-FEDER,” FrailAlert Project (SBPLY/21/180501/000216).Peer reviewedInstitute of Electrical and Electronics EngineersEuropean CommissionMinisterio de Ciencia e Innovación (España)Ruiz-Ruiz, Luisa [0000-0003-0316-7781]García-Domínguez, Juan Jesús [0000-0002-7121-8651]Jiménez Ruiz, Antonio R. [0000-0001-9771-1930]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/374515reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122642OB-C43info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PCI2020-112040Ruiz-Ruiz, Luisa; Neira Álvarez, Marta; Huertas Hoyas, Elisabet; Curiel-Regueros, Agustín; García, Rafael; Alonso-Bouzon, Cristina; García de Villa, Sara; Pilla Barroso, Melisa Janela; Seco Granja, Fernando; Jiménez Ruiz, Antonio R.; 2025; GAIT2CARE: A Database for Evaluating the Effectiveness of Two Exercise Programs in Older Adults using Inertial Gait Analysis and Functional Assessments [Dataset]; Zenodo; Version v1; https://doi.org/10.5281/zenodo.15276116http://dx.doi.org/10.1109/TIM.2024.3449951Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3745152026-05-22T06:33:51Z
score 15,811543

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