A Proof-of-Concept OpenSim Moco Framework for Predictive Modelling of Passive Back-Support Exoskeletons
A.R. van der Vlugt (TU Delft - Mechanical Engineering)
A.H.A. Stienen – Mentor (TU Delft - Mechanical Engineering)
A.K. Silverman – Mentor (TU Delft - Mechanical Engineering)
E. van der Kruk – Graduation committee member (TU Delft - Mechanical Engineering)
L. Marchal Crespo – Graduation committee member (TU Delft - Mechanical Engineering)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
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.