AS
A.K. Silverman
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Musculoskeletal (MSK) models are increasingly used to evaluate interventions aimed at reducing low back pain (LBP) and injury risk during occupational lifting. However, model predictions are subject to uncertainty in body segment parameters (BSPs) and muscle parameters, which vary across populations. This study investigated how demographic variability in spinal muscle parameters and BSPs affects predicted spinal loading during dynamic lifting.
A baseline simulation was performed for a symmetric lifting task using data from one male and one female participant. Monte Carlo simulations perturbed maximum isometric force, optimal fiber length, muscle path point locations, trunk mass distribution, and trunk center of mass (CoM). Output uncertainty was quantified using 5--95\% confidence interval (CI) widths of predicted spinal compression forces, while normalized CI widths were used to assess intrinsic parameter sensitivity.
Distinct spinal loading patterns were observed between participants, reflecting differences in lifting technique and load redistribution between the lumbar spine and hip. Muscle geometry produced the largest output uncertainty (22.9\%), followed by maximum isometric force (17.9\%), while optimal fiber length (9.6\%), mass distribution (10.0\%), and CoM location (9.1\%) produced smaller effects. Average uncertainty remained below the predefined threshold of 67.2\% at all spinal levels except L5--S1, with transient exceedances occurring during rapid load redistribution. After accounting for differences in input variability, muscle geometry remained the most influential parameter, indicating the highest intrinsic sensitivity of spinal loading predictions. BSP uncertainty was strongly dependent on lifting technique and body posture. These findings highlight the importance of accurate, preferably subject-specific, muscle geometry and support sex-specific parameter distributions to reduce uncertainty in muscle force-generating capacity and body segment properties. ...
A baseline simulation was performed for a symmetric lifting task using data from one male and one female participant. Monte Carlo simulations perturbed maximum isometric force, optimal fiber length, muscle path point locations, trunk mass distribution, and trunk center of mass (CoM). Output uncertainty was quantified using 5--95\% confidence interval (CI) widths of predicted spinal compression forces, while normalized CI widths were used to assess intrinsic parameter sensitivity.
Distinct spinal loading patterns were observed between participants, reflecting differences in lifting technique and load redistribution between the lumbar spine and hip. Muscle geometry produced the largest output uncertainty (22.9\%), followed by maximum isometric force (17.9\%), while optimal fiber length (9.6\%), mass distribution (10.0\%), and CoM location (9.1\%) produced smaller effects. Average uncertainty remained below the predefined threshold of 67.2\% at all spinal levels except L5--S1, with transient exceedances occurring during rapid load redistribution. After accounting for differences in input variability, muscle geometry remained the most influential parameter, indicating the highest intrinsic sensitivity of spinal loading predictions. BSP uncertainty was strongly dependent on lifting technique and body posture. These findings highlight the importance of accurate, preferably subject-specific, muscle geometry and support sex-specific parameter distributions to reduce uncertainty in muscle force-generating capacity and body segment properties. ...
Musculoskeletal (MSK) models are increasingly used to evaluate interventions aimed at reducing low back pain (LBP) and injury risk during occupational lifting. However, model predictions are subject to uncertainty in body segment parameters (BSPs) and muscle parameters, which vary across populations. This study investigated how demographic variability in spinal muscle parameters and BSPs affects predicted spinal loading during dynamic lifting.
A baseline simulation was performed for a symmetric lifting task using data from one male and one female participant. Monte Carlo simulations perturbed maximum isometric force, optimal fiber length, muscle path point locations, trunk mass distribution, and trunk center of mass (CoM). Output uncertainty was quantified using 5--95\% confidence interval (CI) widths of predicted spinal compression forces, while normalized CI widths were used to assess intrinsic parameter sensitivity.
Distinct spinal loading patterns were observed between participants, reflecting differences in lifting technique and load redistribution between the lumbar spine and hip. Muscle geometry produced the largest output uncertainty (22.9\%), followed by maximum isometric force (17.9\%), while optimal fiber length (9.6\%), mass distribution (10.0\%), and CoM location (9.1\%) produced smaller effects. Average uncertainty remained below the predefined threshold of 67.2\% at all spinal levels except L5--S1, with transient exceedances occurring during rapid load redistribution. After accounting for differences in input variability, muscle geometry remained the most influential parameter, indicating the highest intrinsic sensitivity of spinal loading predictions. BSP uncertainty was strongly dependent on lifting technique and body posture. These findings highlight the importance of accurate, preferably subject-specific, muscle geometry and support sex-specific parameter distributions to reduce uncertainty in muscle force-generating capacity and body segment properties.
A baseline simulation was performed for a symmetric lifting task using data from one male and one female participant. Monte Carlo simulations perturbed maximum isometric force, optimal fiber length, muscle path point locations, trunk mass distribution, and trunk center of mass (CoM). Output uncertainty was quantified using 5--95\% confidence interval (CI) widths of predicted spinal compression forces, while normalized CI widths were used to assess intrinsic parameter sensitivity.
Distinct spinal loading patterns were observed between participants, reflecting differences in lifting technique and load redistribution between the lumbar spine and hip. Muscle geometry produced the largest output uncertainty (22.9\%), followed by maximum isometric force (17.9\%), while optimal fiber length (9.6\%), mass distribution (10.0\%), and CoM location (9.1\%) produced smaller effects. Average uncertainty remained below the predefined threshold of 67.2\% at all spinal levels except L5--S1, with transient exceedances occurring during rapid load redistribution. After accounting for differences in input variability, muscle geometry remained the most influential parameter, indicating the highest intrinsic sensitivity of spinal loading predictions. BSP uncertainty was strongly dependent on lifting technique and body posture. These findings highlight the importance of accurate, preferably subject-specific, muscle geometry and support sex-specific parameter distributions to reduce uncertainty in muscle force-generating capacity and body segment properties.
Master thesis
(2026)
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A.R. van der Vlugt, A.H.A. Stienen, A.K. Silverman, E. van der Kruk, L. Marchal Crespo
Back-support exoskeletons may reduce physical loading during lifting and stooping, but their effects are usually assessed indirectly using EMG and motion capture because individual muscle forces cannot be measured in vivo. This thesis developed and evaluated a proof-of-concept OpenSim Moco musculoskeletal modelling predictive simulation framework to estimate how a passive back-support exoskeleton affects muscle forces during stooping.
Experimental data from nine healthy participants performing stoop-lifting trials with and without the passive rigid Laevo exoskeleton included synchronised EMG and motion capture. Analyses examined erector spinae activation, marker-based movement proxies, and hip and leg muscle activation. To determine significance, paired-sample t-tests were used. In OpenSim, the Laevo was modelled as a passive angle-dependent hip flexion torsional spring.
The experimental results did not show a statistically significant reduction in erector spinae activation when using the Laevo, for either peak activation (p = 0.948) or average activation (p = 0.370). The shoulder-hip distance results suggested a trend towards a smaller motion range when using the Laevo, especially in the empty-crate condition (p = 0.090), but this did not reach statistical significance. Clear statistical evidence for a general change in hip or leg muscle activation was not found, although average biceps femoris activation was significantly lower with the Laevo, decreasing from 0.563 to 0.484 in normalized activation (p = 0.016). The model showed partial qualitative agreement with some experimental trends, especially for selected movement-pattern and muscle-activation outcomes.
Overall, the developed framework generated predictive simulations with and without exoskeleton support. However, the results should be interpreted as a preliminary proof of concept rather than model validation. The main contribution is a reproducible OpenSim Moco workflow for modelling a passive back-support exoskeleton and comparing it with experimental EMG and motion-capture trends.
...
Experimental data from nine healthy participants performing stoop-lifting trials with and without the passive rigid Laevo exoskeleton included synchronised EMG and motion capture. Analyses examined erector spinae activation, marker-based movement proxies, and hip and leg muscle activation. To determine significance, paired-sample t-tests were used. In OpenSim, the Laevo was modelled as a passive angle-dependent hip flexion torsional spring.
The experimental results did not show a statistically significant reduction in erector spinae activation when using the Laevo, for either peak activation (p = 0.948) or average activation (p = 0.370). The shoulder-hip distance results suggested a trend towards a smaller motion range when using the Laevo, especially in the empty-crate condition (p = 0.090), but this did not reach statistical significance. Clear statistical evidence for a general change in hip or leg muscle activation was not found, although average biceps femoris activation was significantly lower with the Laevo, decreasing from 0.563 to 0.484 in normalized activation (p = 0.016). The model showed partial qualitative agreement with some experimental trends, especially for selected movement-pattern and muscle-activation outcomes.
Overall, the developed framework generated predictive simulations with and without exoskeleton support. However, the results should be interpreted as a preliminary proof of concept rather than model validation. The main contribution is a reproducible OpenSim Moco workflow for modelling a passive back-support exoskeleton and comparing it with experimental EMG and motion-capture trends.
...
Back-support exoskeletons may reduce physical loading during lifting and stooping, but their effects are usually assessed indirectly using EMG and motion capture because individual muscle forces cannot be measured in vivo. This thesis developed and evaluated a proof-of-concept OpenSim Moco musculoskeletal modelling predictive simulation framework to estimate how a passive back-support exoskeleton affects muscle forces during stooping.
Experimental data from nine healthy participants performing stoop-lifting trials with and without the passive rigid Laevo exoskeleton included synchronised EMG and motion capture. Analyses examined erector spinae activation, marker-based movement proxies, and hip and leg muscle activation. To determine significance, paired-sample t-tests were used. In OpenSim, the Laevo was modelled as a passive angle-dependent hip flexion torsional spring.
The experimental results did not show a statistically significant reduction in erector spinae activation when using the Laevo, for either peak activation (p = 0.948) or average activation (p = 0.370). The shoulder-hip distance results suggested a trend towards a smaller motion range when using the Laevo, especially in the empty-crate condition (p = 0.090), but this did not reach statistical significance. Clear statistical evidence for a general change in hip or leg muscle activation was not found, although average biceps femoris activation was significantly lower with the Laevo, decreasing from 0.563 to 0.484 in normalized activation (p = 0.016). The model showed partial qualitative agreement with some experimental trends, especially for selected movement-pattern and muscle-activation outcomes.
Overall, the developed framework generated predictive simulations with and without exoskeleton support. However, the results should be interpreted as a preliminary proof of concept rather than model validation. The main contribution is a reproducible OpenSim Moco workflow for modelling a passive back-support exoskeleton and comparing it with experimental EMG and motion-capture trends.
Experimental data from nine healthy participants performing stoop-lifting trials with and without the passive rigid Laevo exoskeleton included synchronised EMG and motion capture. Analyses examined erector spinae activation, marker-based movement proxies, and hip and leg muscle activation. To determine significance, paired-sample t-tests were used. In OpenSim, the Laevo was modelled as a passive angle-dependent hip flexion torsional spring.
The experimental results did not show a statistically significant reduction in erector spinae activation when using the Laevo, for either peak activation (p = 0.948) or average activation (p = 0.370). The shoulder-hip distance results suggested a trend towards a smaller motion range when using the Laevo, especially in the empty-crate condition (p = 0.090), but this did not reach statistical significance. Clear statistical evidence for a general change in hip or leg muscle activation was not found, although average biceps femoris activation was significantly lower with the Laevo, decreasing from 0.563 to 0.484 in normalized activation (p = 0.016). The model showed partial qualitative agreement with some experimental trends, especially for selected movement-pattern and muscle-activation outcomes.
Overall, the developed framework generated predictive simulations with and without exoskeleton support. However, the results should be interpreted as a preliminary proof of concept rather than model validation. The main contribution is a reproducible OpenSim Moco workflow for modelling a passive back-support exoskeleton and comparing it with experimental EMG and motion-capture trends.