ArticleIEEE reviews in biomedical engineering2009
Computational Models for Neuromuscular Function.
Article in IEEE reviews in biomedical engineering, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed.
- A spinal network of proprioceptive reflexes can produce a variety of bipedal gaits.Communications biology · 2025Article
- Curriculum is more influential than haptic feedback when learning object manipulation.Science advances · 2025Article
- Article
- A computational study of how an α- to γ-motoneurone collateral can mitigate velocity-dependent stretch reflexes during voluntary movement.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- An alpha- to gamma-motoneurone collateral can mitigate velocity-dependent stretch reflexes during voluntary movement: A computational study.bioRxiv : the preprint server for biology · 2024Article
- Human performance in virtual stabilization of a fractional-order system with reaction delay.Journal of the Royal Society, Interface · 2024Article
- NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress.Journal of neuroengineering and rehabilitation · 2024Review
- Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation.Journal of neuroengineering and rehabilitation · 2024Article
- NSF DARE-transforming modeling in neurorehabilitation: perspectives and opportunities from US funding agencies.Journal of neuroengineering and rehabilitation · 2024Article
- EMG-informed neuromuscular model assesses the effects of varied bodyweight support on muscles during overground walking.Journal of biomechanics · 2023Article
- Numerical instability of Hill-type muscle models.Journal of the Royal Society, Interface · 2023Review
- Neuromuscular control: from a biomechanist's perspective.Frontiers in sports and active living · 2023Review
- "Frontiers in sports and active living · 2023Article
- A direct collocation framework for optimal control simulation of pedaling using OpenSim.PloS one · 2022Article
- A Machine-Learning Method of Predicting Vital Capacity Plateau Value for Ventilatory Pump Failure Based on Data Mining.Healthcare (Basel, Switzerland) · 2021Article
- Force variability is mostly not motor noise: Theoretical implications for motor control.PLoS computational biology · 2021Article
- Rolling balance board of adjustable geometry as a tool to assess balancing skill and to estimate reaction time delay.Journal of the Royal Society, Interface · 2021Article
- Efficient trajectory optimization for curved running using a 3D musculoskeletal model with implicit dynamics.Scientific reports · 2020Article
- Closed-loop machine-controlled CPR system optimises haemodynamics during prolonged CPR.Resuscitation plus · 2020Article
- Establishing metrics and control laws for the learning process: ball and beam balancing.Biological cybernetics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Computational models of the neuromuscular system hold the potential to allow us to reach a deeper understanding of neuromuscular function and clinical rehabilitation by complementing experimentation. By serving as a means to distill and explore specific hypotheses, computational models emerge from prior experimental data and motivate future experimental work. Here we review computational tools used to understand neuromuscular function including musculoskeletal modeling, machine learning, control theory, and statistical model analysis. We conclude that these tools, when used in combination, have the potential to further our understanding of neuromuscular function by serving as a rigorous means to test scientific hypotheses in ways that complement and leverage experimental data.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.