Synergy-informed bilevel optimization reveals joint-specific benefits for realistic exoskeleton torque prediction
- 1KTH Royal Institute of Technology: Kungliga Tekniska Hogskolan
Abstract
Simulation-driven optimization can guide exoskeleton control design, but its utility is limited by the accuracy with which muscle coordination is predicted. Strict effort-minimization formulations predict non-physiological muscle shutdown under high exoskeleton assistance, leading to peak assistive torques that exceed values reported in human-in-the-loop experiments. We evaluated the accuracy of synergy-informed simulations at predicting assistance across four single-joint exoskeleton configurations: ankle plantarflexion, knee extension, hip flexion, and hip abduction. We implemented a bilevel optimization to predict a parameterized assistive torque that minimizes metabolic cost, with assisted muscle dynamics estimated via either effort-minimization simulations (minimizing muscle effort only) or synergy-informed simulations (minimizing muscle effort constrained to a synergy structure). We used experimental data from five unimpaired adults walking at their preferred speed and compared results descriptively between the two formulations within each subject. Assistance based on effort-minimization simulations shut down agonist muscle activations and overestimated peak assistive torque compared with experimental values. In contrast, constraining muscle controls to a synergy structure prevented non-physiological muscle shutdown and yielded assistive torques closer to those measured in human-in-the-loop experiments. This benefit was not observed equally across all assisted degrees of freedom or subjects, emerging only where no single synergy could be suppressed to unload the assisted muscles. Synergy weights can therefore be interpreted as joint-specific predictors of when simulations can reliably inform wearable robot design.
Related
- Robust prediction of drug combination side effects in realistic settings
- A Physics-Informed Neural Network Surrogate for Patient-Specific Hepatic Arterial Hemodynamics in Yttrium-90 Radioembolization: Network Architecture, Boundary-Condition Enforcement, and Data Efficiency
- AlphaGenome informed machine learning for pathogenicity prediction of germline mutations
- Single-cell informed metabolic modeling reveals organ-specific metabolic adaptations in breast cancer organotropism
- Physics-informed neural networks for subject-specific recovery of travelling-wave structure in resting-state fMRI
BibTeX
@misc{luis2026synergyinformed,
title = {{Synergy-informed bilevel optimization reveals joint-specific benefits for realistic exoskeleton torque prediction}},
author = {I. Luis and E. M. Gutierrez-Farewik and T. K. Uchida},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.05.764547},
url = {https://www.biorxiv.org/content/10.64898/2026.11.05.764547v1},
note = {CC0},
}
RIS
TY - UNPB
TI - Synergy-informed bilevel optimization reveals joint-specific benefits for realistic exoskeleton torque prediction
AU - I. Luis
AU - E. M. Gutierrez-Farewik
AU - T. K. Uchida
PY - 2026
DA - 2026/11/05
PB - bioRxiv
DO - 10.64898/2026.11.05.764547
UR - https://www.biorxiv.org/content/10.64898/2026.11.05.764547v1
AB - Simulation-driven optimization can guide exoskeleton control design, but its utility is limited by the accuracy with which muscle coordination is predicted. Strict effort-minimization formulations predict non-physiological muscle shutdown under high exoskeleton assistance, leading to peak assistive torques that exceed values reported in human-in-the-loop experiments. We evaluated the accuracy of synergy-informed simulations at predicting assistance across four single-joint exoskeleton configurations: ankle plantarflexion, knee extension, hip flexion, and hip abduction. We implemented a bilevel optimization to predict a parameterized assistive torque that minimizes metabolic cost, with assisted muscle dynamics estimated via either effort-minimization simulations (minimizing muscle effort only) or synergy-informed simulations (minimizing muscle effort constrained to a synergy structure). We used experimental data from five unimpaired adults walking at their preferred speed and compared results descriptively between the two formulations within each subject. Assistance based on effort-minimization simulations shut down agonist muscle activations and overestimated peak assistive torque compared with experimental values. In contrast, constraining muscle controls to a synergy structure prevented non-physiological muscle shutdown and yielded assistive torques closer to those measured in human-in-the-loop experiments. This benefit was not observed equally across all assisted degrees of freedom or subjects, emerging only where no single synergy could be suppressed to unload the assisted muscles. Synergy weights can therefore be interpreted as joint-specific predictors of when simulations can reliably inform wearable robot design.
ER -