Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke
Introduction: Tuning the control parameters is one of the main challenges in robotic gait therapy. Control strategies that vary the control parameters based on the user’s performance are still scarce and do not exploit the potential of using spatiotemporal metrics. The goal of this study was to vali...
| Autores: | , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/375258 |
| Acceso en línea: | http://hdl.handle.net/10261/375258 |
| Access Level: | acceso abierto |
| Palabra clave: | stroke wearable sensors inertial sensors IMU gait analysis gait assessment rehabilitation exoskeleton |
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oai:digital.csic.es:10261/375258 |
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España |
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Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke |
| title |
Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke |
| spellingShingle |
Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke De Miguel Fernández, Jesús stroke wearable sensors inertial sensors IMU gait analysis gait assessment rehabilitation exoskeleton |
| title_short |
Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke |
| title_full |
Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke |
| title_fullStr |
Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke |
| title_full_unstemmed |
Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke |
| title_sort |
Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke |
| dc.creator.none.fl_str_mv |
De Miguel Fernández, Jesús Salazar Del Rio, Miguel Rey Prieto, Marta Bayón, Cristina Guirao Cano, Lluis Font Llagunes, Josep M. Lobo Prat, Joan |
| author |
De Miguel Fernández, Jesús |
| author_facet |
De Miguel Fernández, Jesús Salazar Del Rio, Miguel Rey Prieto, Marta Bayón, Cristina Guirao Cano, Lluis Font Llagunes, Josep M. Lobo Prat, Joan |
| author_role |
author |
| author2 |
Salazar Del Rio, Miguel Rey Prieto, Marta Bayón, Cristina Guirao Cano, Lluis Font Llagunes, Josep M. Lobo Prat, Joan |
| author2_role |
author author author author author author |
| dc.contributor.none.fl_str_mv |
Ministerio de Ciencia e Innovación (España) Agencia Estatal de Investigación (España) De Miguel Fernández, Jesús [0000-0001-8651-1642] Salazar Del Rio, Miguel Rey Prieto, Marta Bayón, Cristina [0000-0003-1825-1265] Guirao Cano, Lluis [0000-0002-8698-9483] Font Llagunes, Josep M. [0000-0002-7192-2980] Lobo Prat, Joan [0000-0003-4197-1391] Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] |
| dc.subject.none.fl_str_mv |
stroke wearable sensors inertial sensors IMU gait analysis gait assessment rehabilitation exoskeleton |
| topic |
stroke wearable sensors inertial sensors IMU gait analysis gait assessment rehabilitation exoskeleton |
| description |
Introduction: Tuning the control parameters is one of the main challenges in robotic gait therapy. Control strategies that vary the control parameters based on the user’s performance are still scarce and do not exploit the potential of using spatiotemporal metrics. The goal of this study was to validate the feasibility of using shank-worn Inertial Measurement Units (IMUs) for clinical gait analysis after stroke and evaluate their preliminary applicability in designing an automatic and adaptive controller for a knee exoskeleton (ABLE-KS). Methods: First, we estimated the temporal (i.e., stride time, stance, and swing duration) and spatial (i.e., stride length, maximum vertical displacement, foot clearance, and circumduction) metrics in six post-stroke participants while walking on a treadmill and overground and compared these estimates with data from an optical motion tracking system. Next, we analyzed the relationships between the IMU-estimated metrics and an exoskeleton control parameter related to the peak knee flexion torque. Finally, we trained two machine learning algorithms, i.e., linear regression and neural network, to model the relationship between the exoskeleton torque and maximum vertical displacement, which was the metric that showed the strongest correlations with the data from the optical system [r = 0.84; ICC(A,1) = 0.73; ICC(C,1) = 0.81] and peak knee flexion torque (r = 0.957). Results: Offline validation of both neural network and linear regression models showed good predictions (R2 = 0.70–0.80; MAE = 0.48–0.58 Nm) of the peak torque based on the maximum vertical displacement metric for the participants with better gait function, i.e., gait speed > 0.7 m/s. For the participants with worse gait function, both models failed to provide good predictions (R2 = 0.00–0.19; MAE = 1.15–1.29 Nm) of the peak torque despite having a moderate-to-strong correlation between the spatiotemporal metric and control parameter. Discussion: Our preliminary results indicate that the stride-by-stride estimations of shank-worn IMUs show potential to design automatic and adaptive exoskeleton control strategies for people with moderate impairments in gait function due to stroke |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023 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 |
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article |
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publishedVersion |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10261/375258 |
| url |
http://hdl.handle.net/10261/375258 |
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Inglés |
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Inglés |
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https://doi.org/I 10.3389/fbioe.2023.1208561 Sí |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
Frontiers in Bioscience Publications |
| publisher.none.fl_str_mv |
Frontiers in Bioscience Publications |
| dc.source.none.fl_str_mv |
reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC instname:Consejo Superior de Investigaciones Científicas (CSIC) |
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Consejo Superior de Investigaciones Científicas (CSIC) |
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DIGITAL.CSIC. Repositorio Institucional del CSIC |
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DIGITAL.CSIC. Repositorio Institucional del CSIC |
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| _version_ |
1869420911459827713 |
| spelling |
Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after strokeDe Miguel Fernández, JesúsSalazar Del Rio, MiguelRey Prieto, MartaBayón, CristinaGuirao Cano, LluisFont Llagunes, Josep M.Lobo Prat, Joanstrokewearable sensorsinertial sensorsIMUgait analysisgait assessmentrehabilitationexoskeletonIntroduction: Tuning the control parameters is one of the main challenges in robotic gait therapy. Control strategies that vary the control parameters based on the user’s performance are still scarce and do not exploit the potential of using spatiotemporal metrics. The goal of this study was to validate the feasibility of using shank-worn Inertial Measurement Units (IMUs) for clinical gait analysis after stroke and evaluate their preliminary applicability in designing an automatic and adaptive controller for a knee exoskeleton (ABLE-KS). Methods: First, we estimated the temporal (i.e., stride time, stance, and swing duration) and spatial (i.e., stride length, maximum vertical displacement, foot clearance, and circumduction) metrics in six post-stroke participants while walking on a treadmill and overground and compared these estimates with data from an optical motion tracking system. Next, we analyzed the relationships between the IMU-estimated metrics and an exoskeleton control parameter related to the peak knee flexion torque. Finally, we trained two machine learning algorithms, i.e., linear regression and neural network, to model the relationship between the exoskeleton torque and maximum vertical displacement, which was the metric that showed the strongest correlations with the data from the optical system [r = 0.84; ICC(A,1) = 0.73; ICC(C,1) = 0.81] and peak knee flexion torque (r = 0.957). Results: Offline validation of both neural network and linear regression models showed good predictions (R2 = 0.70–0.80; MAE = 0.48–0.58 Nm) of the peak torque based on the maximum vertical displacement metric for the participants with better gait function, i.e., gait speed > 0.7 m/s. For the participants with worse gait function, both models failed to provide good predictions (R2 = 0.00–0.19; MAE = 1.15–1.29 Nm) of the peak torque despite having a moderate-to-strong correlation between the spatiotemporal metric and control parameter. Discussion: Our preliminary results indicate that the stride-by-stride estimations of shank-worn IMUs show potential to design automatic and adaptive exoskeleton control strategies for people with moderate impairments in gait function due to strokeThis work was supported by grant No. 2020 FI_B 00331 funded by the Agency for Management of University and Research Grants (AGAUR) along with the Secretariat of Universities and Research of the Catalan Ministry of Research and Universities and the European Social Fund (ESF), grant No. 2021 SGR 01052 funded by the Agency for Management of University and Research Grants (AGAUR) and the Catalan Ministry of Research and Universities, and grant PTQ2018- 010227 funded by the Spanish Ministry of Science and Innovation (MCI)–Agencia Estatal de Investigación (AEI). The project that gave rise to these results has received funding from the “la Caixa” Foundation under the grant agreement LCF/TR/CC20/52480002.Peer reviewedFrontiers in Bioscience PublicationsMinisterio de Ciencia e Innovación (España)Agencia Estatal de Investigación (España)De Miguel Fernández, Jesús [0000-0001-8651-1642]Salazar Del Rio, MiguelRey Prieto, MartaBayón, Cristina [0000-0003-1825-1265]Guirao Cano, Lluis [0000-0002-8698-9483]Font Llagunes, Josep M. [0000-0002-7192-2980]Lobo Prat, Joan [0000-0003-4197-1391]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/375258reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/I 10.3389/fbioe.2023.1208561Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3752582026-05-22T06:33:51Z |
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15,812429 |