Methods for integrating postural control into biomechanical human simulations: a systematic review.
Biomechanical human model
Biomechanics
Forward dynamics
Musculoskeletal model
Postural control
Simulation
Systematic review
Journal
Journal of neuroengineering and rehabilitation
ISSN: 1743-0003
Titre abrégé: J Neuroeng Rehabil
Pays: England
ID NLM: 101232233
Informations de publication
Date de publication:
21 08 2023
21 08 2023
Historique:
received:
23
12
2022
accepted:
09
08
2023
medline:
23
8
2023
pubmed:
22
8
2023
entrez:
22
8
2023
Statut:
epublish
Résumé
Understanding of the human body's internal processes to maintain balance is fundamental to simulate postural control behaviour. The body uses multiple sensory systems' information to obtain a reliable estimate about the current body state. This information is used to control the reactive behaviour to maintain balance. To predict a certain motion behaviour with knowledge of the muscle forces, forward dynamic simulations of biomechanical human models can be utilized. We aim to use predictive postural control simulations to give therapy recommendations to patients suffering from postural disorders in the future. It is important to know which types of modelling approaches already exist to apply such predictive forward dynamic simulations. Current literature provides different models that aim to simulate human postural control. We conducted a systematic literature research to identify the different approaches of postural control models. The different approaches are discussed regarding their applied biomechanical models, sensory representation, sensory integration, and control methods in standing and gait simulations. We searched on Scopus, Web of Science and PubMed using a search string, scanned 1253 records, and found 102 studies to be eligible for inclusion. The included studies use different ways for sensory representation and integration, although underlying neural processes still remain unclear. We found that for postural control optimal control methods like linear quadratic regulators and model predictive control methods are used less, when models' level of details is increasing, and nonlinearities become more important. Considering musculoskeletal models, reflex-based and PD controllers are mainly applied and show promising results, as they aim to create human-like motion behaviour considering physiological processes.
Identifiants
pubmed: 37605197
doi: 10.1186/s12984-023-01235-3
pii: 10.1186/s12984-023-01235-3
pmc: PMC10440942
doi:
Types de publication
Systematic Review
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
111Informations de copyright
© 2023. BioMed Central Ltd., part of Springer Nature.
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