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Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation

We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the low...

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Autores principales: Uhlenberg, Lena, Derungs, Adrian, Amft, Oliver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132030/
https://www.ncbi.nlm.nih.gov/pubmed/37122859
http://dx.doi.org/10.3389/fbioe.2023.1104000
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author Uhlenberg, Lena
Derungs, Adrian
Amft, Oliver
author_facet Uhlenberg, Lena
Derungs, Adrian
Amft, Oliver
author_sort Uhlenberg, Lena
collection PubMed
description We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed.
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spelling pubmed-101320302023-04-27 Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation Uhlenberg, Lena Derungs, Adrian Amft, Oliver Front Bioeng Biotechnol Bioengineering and Biotechnology We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed. Frontiers Media S.A. 2023-04-12 /pmc/articles/PMC10132030/ /pubmed/37122859 http://dx.doi.org/10.3389/fbioe.2023.1104000 Text en Copyright © 2023 Uhlenberg, Derungs and Amft. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Uhlenberg, Lena
Derungs, Adrian
Amft, Oliver
Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation
title Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation
title_full Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation
title_fullStr Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation
title_full_unstemmed Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation
title_short Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation
title_sort co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132030/
https://www.ncbi.nlm.nih.gov/pubmed/37122859
http://dx.doi.org/10.3389/fbioe.2023.1104000
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