• A physiologically inspired hyb...

*Result*: A physiologically inspired hybrid CPG/Reflex controller for cycling simulations that generalizes to walking.

Title:
A physiologically inspired hybrid CPG/Reflex controller for cycling simulations that generalizes to walking.
Authors:
Severini G; School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland.; Insight Center for Data Analytics, University College Dublin, Dublin, Ireland., Muñoz D; School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland.
Source:
PLoS computational biology [PLoS Comput Biol] 2025 Sep 12; Vol. 21 (9), pp. e1013494. Date of Electronic Publication: 2025 Sep 12 (Print Publication: 2025).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Imprint Name(s):
Original Publication: San Francisco, CA : Public Library of Science, [2005]-
MeSH Terms:
Walking*/physiology , Bicycling*/physiology , Reflex*/physiology , Central Pattern Generators*/physiology , Models, Biological*, Humans ; Computer Simulation ; Computational Biology
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Entry Date(s):
Date Created: 20250912 Date Completed: 20250918 Latest Revision: 20250920
Update Code:
20260130
PubMed Central ID:
PMC12445551
DOI:
10.1371/journal.pcbi.1013494
PMID:
40939116
Database:
MEDLINE

*Further Information*

*Predictive simulations based on explicit, physiologically inspired, control policies, can be used to test theories on motor control and to evaluate the effect of interventions on the different components of control. Several control architectures have been proposed for simulating locomotor tasks, based on fully feedback, reflex-based, controllers, or on feedforward architectures mimicking the Central Pattern Generators. Hybrid architectures integrating both feedback and feedforward components represent a viable alternative to fully feedback or feedforward controllers. Current literature on controller-based simulations almost exclusively presents task-specific controllers that do not generalize across different tasks. The task-specificity of current controllers limits the generalizability of the neurophysiological principles behind such controllers. Here we propose a hybrid controller for predictive simulations of cycling where the feedforward component is based on a well-known theoretical model, the Unit Burst Generation model, and the feedback component includes a limited set of reflex pathways, expected to be active during steady cycling. We show that this controller can simulate physiological stationary cycling patterns at different desired speeds and seat heights. We also show that the controller can generalize to walking behaviors by just adding an additional control component for accounting balance needs. The controller here proposed, although simple in design, represent an instance of physiologically inspired generalizable controller for cyclical lower limb tasks.
(Copyright: © 2025 Severini, Muñoz. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)*

*The authors have declared that no competing interests exist.*