Details
| Original language | English |
|---|---|
| Article number | 1507162 |
| Journal | Frontiers in Bioengineering and Biotechnology |
| Volume | 13 |
| Publication status | Published - 19 Feb 2025 |
Abstract
Estimating spatiotemporal, kinematic, and kinetic movement variables with little obtrusion to the user is critical for clinical and sports applications. One possible approach is using a sparse inertial sensor setup, where sensors are not placed on all relevant body segments. Here, we investigated if movement variables can be estimated similarly accurate from sparse sensor setups as from a full lower-body sensor setup. We estimated the variables by solving optimal control problems with sagittal plane lower-body musculoskeletal models, in which we minimized an objective that combined tracking of accelerometer and gyroscope data with minimizing muscular effort. We created simulations for 10 participants at three walking and three running speeds, using seven sensor setups with between two and seven sensors located at the feet, shank, thighs, and/or pelvis. We found that differences between variables estimated from inertial sensors and those from optical motion capture were small for all sensor setups. Including all sensors did not necessarily lead to the smallest root mean square deviations (RMSDs) and highest coefficients of determination ( (Formula presented.) ). Setups without a pelvis sensor led to too much forward trunk lean and inaccurate spatiotemporal variables. Mean RMSDs were highest for the setup with two foot-worn inertial sensors (largest error in knee angle during running: 18 deg vs. 11 deg for the full lower-body setup), and ranged between 4.8–18 deg for the joint angles, between 1.0–5.4 BW BH% for the joint moments, and between 0.03 BW–0.49 BW for the ground reaction forces. We found strong or moderate relationships ( (Formula presented.) ) on average for all kinematic and kinetic variables, except for the hip and knee moment for five out of the seven setups. The large range of the coefficient of determination for most kinetic variables indicated individual differences in simulation quality. Therefore, we conclude that we can perform a comprehensive sagittal-plane motion analysis with sparse sensor setups as accurately as with a full sensor setup with sensors on the feet and on either the pelvis or the thighs. Such a sparse sensor setup enables comprehensive movement analysis outside the laboratory, by increasing usability of inertial sensors.
Keywords
- gait analysis, gait simulations, inertial measurement units, optimal control, trajectory optimization
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biotechnology
- Chemical Engineering(all)
- Bioengineering
- Medicine(all)
- Histology
- Engineering(all)
- Biomedical Engineering
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In: Frontiers in Bioengineering and Biotechnology, Vol. 13, 1507162, 19.02.2025.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions
AU - Dorschky, Eva
AU - Nitschke, Marlies
AU - Mayer, Matthias
AU - Weygers, Ive
AU - Gassner, Heiko
AU - Seel, Thomas
AU - Eskofier, Bjoern M.
AU - Koelewijn, Anne D.
N1 - Publisher Copyright: Copyright © 2025 Dorschky, Nitschke, Mayer, Weygers, Gassner, Seel, Eskofier and Koelewijn.
PY - 2025/2/19
Y1 - 2025/2/19
N2 - Estimating spatiotemporal, kinematic, and kinetic movement variables with little obtrusion to the user is critical for clinical and sports applications. One possible approach is using a sparse inertial sensor setup, where sensors are not placed on all relevant body segments. Here, we investigated if movement variables can be estimated similarly accurate from sparse sensor setups as from a full lower-body sensor setup. We estimated the variables by solving optimal control problems with sagittal plane lower-body musculoskeletal models, in which we minimized an objective that combined tracking of accelerometer and gyroscope data with minimizing muscular effort. We created simulations for 10 participants at three walking and three running speeds, using seven sensor setups with between two and seven sensors located at the feet, shank, thighs, and/or pelvis. We found that differences between variables estimated from inertial sensors and those from optical motion capture were small for all sensor setups. Including all sensors did not necessarily lead to the smallest root mean square deviations (RMSDs) and highest coefficients of determination ( (Formula presented.) ). Setups without a pelvis sensor led to too much forward trunk lean and inaccurate spatiotemporal variables. Mean RMSDs were highest for the setup with two foot-worn inertial sensors (largest error in knee angle during running: 18 deg vs. 11 deg for the full lower-body setup), and ranged between 4.8–18 deg for the joint angles, between 1.0–5.4 BW BH% for the joint moments, and between 0.03 BW–0.49 BW for the ground reaction forces. We found strong or moderate relationships ( (Formula presented.) ) on average for all kinematic and kinetic variables, except for the hip and knee moment for five out of the seven setups. The large range of the coefficient of determination for most kinetic variables indicated individual differences in simulation quality. Therefore, we conclude that we can perform a comprehensive sagittal-plane motion analysis with sparse sensor setups as accurately as with a full sensor setup with sensors on the feet and on either the pelvis or the thighs. Such a sparse sensor setup enables comprehensive movement analysis outside the laboratory, by increasing usability of inertial sensors.
AB - Estimating spatiotemporal, kinematic, and kinetic movement variables with little obtrusion to the user is critical for clinical and sports applications. One possible approach is using a sparse inertial sensor setup, where sensors are not placed on all relevant body segments. Here, we investigated if movement variables can be estimated similarly accurate from sparse sensor setups as from a full lower-body sensor setup. We estimated the variables by solving optimal control problems with sagittal plane lower-body musculoskeletal models, in which we minimized an objective that combined tracking of accelerometer and gyroscope data with minimizing muscular effort. We created simulations for 10 participants at three walking and three running speeds, using seven sensor setups with between two and seven sensors located at the feet, shank, thighs, and/or pelvis. We found that differences between variables estimated from inertial sensors and those from optical motion capture were small for all sensor setups. Including all sensors did not necessarily lead to the smallest root mean square deviations (RMSDs) and highest coefficients of determination ( (Formula presented.) ). Setups without a pelvis sensor led to too much forward trunk lean and inaccurate spatiotemporal variables. Mean RMSDs were highest for the setup with two foot-worn inertial sensors (largest error in knee angle during running: 18 deg vs. 11 deg for the full lower-body setup), and ranged between 4.8–18 deg for the joint angles, between 1.0–5.4 BW BH% for the joint moments, and between 0.03 BW–0.49 BW for the ground reaction forces. We found strong or moderate relationships ( (Formula presented.) ) on average for all kinematic and kinetic variables, except for the hip and knee moment for five out of the seven setups. The large range of the coefficient of determination for most kinetic variables indicated individual differences in simulation quality. Therefore, we conclude that we can perform a comprehensive sagittal-plane motion analysis with sparse sensor setups as accurately as with a full sensor setup with sensors on the feet and on either the pelvis or the thighs. Such a sparse sensor setup enables comprehensive movement analysis outside the laboratory, by increasing usability of inertial sensors.
KW - gait analysis
KW - gait simulations
KW - inertial measurement units
KW - optimal control
KW - trajectory optimization
UR - http://www.scopus.com/inward/record.url?scp=85219748690&partnerID=8YFLogxK
U2 - 10.3389/fbioe.2025.1507162
DO - 10.3389/fbioe.2025.1507162
M3 - Article
VL - 13
JO - Frontiers in Bioengineering and Biotechnology
JF - Frontiers in Bioengineering and Biotechnology
SN - 2296-4185
M1 - 1507162
ER -