Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study
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...
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| Format: | Article |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2023.1208561/full |
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| author | Jesús De Miguel-Fernández Jesús De Miguel-Fernández Miguel Salazar-Del Rio Miguel Salazar-Del Rio Marta Rey-Prieto Marta Rey-Prieto Cristina Bayón Lluis Guirao-Cano Josep M. Font-Llagunes Josep M. Font-Llagunes Joan Lobo-Prat |
| author_facet | Jesús De Miguel-Fernández Jesús De Miguel-Fernández Miguel Salazar-Del Rio Miguel Salazar-Del Rio Marta Rey-Prieto Marta Rey-Prieto Cristina Bayón Lluis Guirao-Cano Josep M. Font-Llagunes Josep M. Font-Llagunes Joan Lobo-Prat |
| author_sort | Jesús De Miguel-Fernández |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T01:59:07Z |
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| id | doaj.art-4878b7a621a2452eb463de99f3a30793 |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-03-12T01:59:07Z |
| publishDate | 2023-08-01 |
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| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-4878b7a621a2452eb463de99f3a307932023-09-07T21:27:59ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852023-08-011110.3389/fbioe.2023.12085611208561Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility studyJesús De Miguel-Fernández0Jesús De Miguel-Fernández1Miguel Salazar-Del Rio2Miguel Salazar-Del Rio3Marta Rey-Prieto4Marta Rey-Prieto5Cristina Bayón6Lluis Guirao-Cano7Josep M. Font-Llagunes8Josep M. Font-Llagunes9Joan Lobo-Prat10Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, SpainInstitut de Recerca Sant Joan de Déu, Esplugues de Llobregat, SpainBiomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, SpainInstitut de Recerca Sant Joan de Déu, Esplugues de Llobregat, SpainBiomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, SpainInstitut de Recerca Sant Joan de Déu, Esplugues de Llobregat, SpainDepartment of Biomechanical Engineering, University of Twente, Enschede, NetherlandsHospital Universitari Múttua de Terrassa, Barcelona, SpainBiomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, SpainInstitut de Recerca Sant Joan de Déu, Esplugues de Llobregat, SpainABLE Human Motion, Barcelona, SpainIntroduction: 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.https://www.frontiersin.org/articles/10.3389/fbioe.2023.1208561/fullstrokewearable sensorsinertial sensorsIMUgait analysisgait assessment |
| spellingShingle | Jesús De Miguel-Fernández Jesús De Miguel-Fernández Miguel Salazar-Del Rio Miguel Salazar-Del Rio Marta Rey-Prieto Marta Rey-Prieto Cristina Bayón Lluis Guirao-Cano Josep M. Font-Llagunes Josep M. Font-Llagunes Joan Lobo-Prat Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study Frontiers in Bioengineering and Biotechnology stroke wearable sensors inertial sensors IMU gait analysis gait assessment |
| title | Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study |
| title_full | Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study |
| title_fullStr | Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study |
| title_full_unstemmed | Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study |
| title_short | Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study |
| title_sort | inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke a feasibility study |
| topic | stroke wearable sensors inertial sensors IMU gait analysis gait assessment |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2023.1208561/full |
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