ArticleJournal of the Royal Society, Interface2020
Predicting walking response to ankle exoskeletons using data-driven models.
Article in Journal of the Royal Society, Interface, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Shallow recurrent decoders for neural and behavioural dynamics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Article
- Dynamic Modelling and Control Strategy Analysis of a Lower-Limb Exoskeleton.Sensors (Basel, Switzerland) · 2026Article
- Marker Data Enhancement for Markerless Motion Capture.IEEE transactions on bio-medical engineering · 2025Article
- Biomechanical models in the lower-limb exoskeletons development: a review.Journal of neuroengineering and rehabilitation · 2025Review
- Marker Data Enhancement For Markerless Motion Capture.bioRxiv : the preprint server for biology · 2024Article
- Human motion data expansion from arbitrary sparse sensors with shallow recurrent decoders.bioRxiv : the preprint server for biology · 2024Article
- Quantifying changes in individual-specific template-based representations of center-of-mass dynamics during walking with ankle exoskeletons using Hybrid-SINDy.Scientific reports · 2024Article
- Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics.PLoS computational biology · 2023Article
- Multimodal video and IMU kinematic dataset on daily life activities using affordable devices.Scientific data · 2023Article
- A machine learning approach to quantify individual gait responses to ankle exoskeletons.bioRxiv : the preprint server for biology · 2023Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite recent innovations in exoskeleton design and control, predicting subject-specific impacts of exoskeletons on gait remains challenging. We evaluated the ability of three classes of subject-specific phase-varying (PV) models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics. Each model-PV, linear PV (LPV) and nonlinear PV (NPV)-leveraged Floquet theory to predict deviations from a nominal gait cycle due to exoskeleton torque, though the models differed in complexity and expected prediction accuracy. For 12 unimpaired adults walking with bilateral passive ankle exoskeletons, we predicted kinematics and muscle activity in response to three exoskeleton torque conditions. The LPV model's predictions were more accurate than the PV model when predicting less than 12.5% of a stride in the future and explained 49-70% of the variance in hip, knee and ankle kinematic responses to torque. The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses. This work highlights the potential of data-driven PV models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.
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