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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2023.1104000/full |
| _version_ | 1827968301631799296 |
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| author | Lena Uhlenberg Lena Uhlenberg Adrian Derungs Oliver Amft Oliver Amft |
| author_facet | Lena Uhlenberg Lena Uhlenberg Adrian Derungs Oliver Amft Oliver Amft |
| author_sort | Lena Uhlenberg |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-09T18:19:03Z |
| format | Article |
| id | doaj.art-a01fdacd978043a8b34f0b90272df615 |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-04-09T18:19:03Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-a01fdacd978043a8b34f0b90272df6152023-04-12T12:58:57ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852023-04-011110.3389/fbioe.2023.11040001104000Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimationLena Uhlenberg0Lena Uhlenberg1Adrian Derungs2Oliver Amft3Oliver Amft4Hahn-Schickard, Freiburg, GermanyIntelligent Embedded Systems Lab, University of Freiburg, Freiburg, GermanyF. Hoffmann–La Roche Ltd, pRED, Roche Innovation Center Basel, Basel, SwitzerlandHahn-Schickard, Freiburg, GermanyIntelligent Embedded Systems Lab, University of Freiburg, Freiburg, GermanyWe 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.https://www.frontiersin.org/articles/10.3389/fbioe.2023.1104000/fullIMUaccelerometergyroscopedigital twinstroke rehabilitationmultiscale modeling |
| spellingShingle | Lena Uhlenberg Lena Uhlenberg Adrian Derungs Oliver Amft Oliver Amft Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation Frontiers in Bioengineering and Biotechnology IMU accelerometer gyroscope digital twin stroke rehabilitation multiscale modeling |
| 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 | IMU accelerometer gyroscope digital twin stroke rehabilitation multiscale modeling |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2023.1104000/full |
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