ArticleInterface focus2015
Stochastic modelling of muscle recruitment during activity.
Article in Interface focus, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of the Associations Between Inverse Dynamics and Musculoskeletal Modeling to Investigate Joint Loading in a Clinical Environment.Frontiers in bioengineering and biotechnology · 2020Pooled it
- Exploring the effect of different neural strategies on the knee joint contact forces during walking in adults.Scientific reports · 2026Article
- Is EMG Information Necessary for Deep Learning Estimation of Joint and Muscle Level States?IEEE transactions on bio-medical engineering · 2026Article
- EMG Validation of a Subject-Specific Thoracolumbar Spine Musculoskeletal Model During Dynamic Activities in Older Adults.Annals of biomedical engineering · 2023Article
- A machine learning approach to quantify individual gait responses to ankle exoskeletons.bioRxiv : the preprint server for biology · 2023Article
- Neuromuscular control: from a biomechanist's perspective.Frontiers in sports and active living · 2023Review
- Using Bayesian inference to estimate plausible muscle forces in musculoskeletal models.Journal of neuroengineering and rehabilitation · 2022Article
- Anterior Cruciate Ligament Loading Increases With Pivot-Shift Mechanism During Asymmetrical Drop Vertical Jump in Female Athletes.Orthopaedic journal of sports medicine · 2021Article
- Predicting walking response to ankle exoskeletons using data-driven models.Journal of the Royal Society, Interface · 2020Article
- Modelling Human Locomotion to Inform Exercise Prescription for Osteoporosis.Current osteoporosis reports · 2020Review
- Knee abduction moment is predicted by lower gluteus medius force and larger vertical and lateral ground reaction forces during drop vertical jump in female athletes.Journal of biomechanics · 2020Article
- EMG-Informed Musculoskeletal Modeling to Estimate Realistic Knee Anterior Shear Force During Drop Vertical Jump in Female Athletes.Annals of biomedical engineering · 2019Article
- Connecting the legs with a spring improves human running economy.The Journal of experimental biology · 2019Article
- A computationally efficient strategy to estimate muscle forces in a finite element musculoskeletal model of the lower limb.Journal of biomechanics · 2019Article
- Gait Rehabilitation Using Functional Electrical Stimulation Induces Changes in Ankle Muscle Coordination in Stroke Survivors: A Preliminary Study.Frontiers in neurology · 2018Article
- In Vivo Neuromechanics: Decoding Causal Motor Neuron Behavior with Resulting Musculoskeletal Function.Scientific reports · 2017Article
- Hammering Does Not Fit Fitts' Law.Frontiers in computational neuroscience · 2017Article
- Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis.Journal of biomechanical engineering · 2016Article
- Suboptimal Muscle Synergy Activation Patterns Generalize their Motor Function across Postures.Frontiers in computational neuroscience · 2016Article
- Modeling and simulating the neuromuscular mechanisms regulating ankle and knee joint stiffness during human locomotion.Journal of neurophysiology · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Muscle forces can be selected from a space of muscle recruitment strategies that produce stable motion and variable muscle and joint forces. However, current optimization methods provide only a single muscle recruitment strategy. We modelled the spectrum of muscle recruitment strategies while walking. The equilibrium equations at the joints, muscle constraints, static optimization solutions and 15-channel electromyography (EMG) recordings for seven walking cycles were taken from earlier studies. The spectrum of muscle forces was calculated using Bayesian statistics and Markov chain Monte Carlo (MCMC) methods, whereas EMG-driven muscle forces were calculated using EMG-driven modelling. We calculated the differences between the spectrum and EMG-driven muscle force for 1-15 input EMGs, and we identified the muscle strategy that best matched the recorded EMG pattern. The best-fit strategy, static optimization solution and EMG-driven force data were compared using correlation analysis. Possible and plausible muscle forces were defined as within physiological boundaries and within EMG boundaries. Possible muscle and joint forces were calculated by constraining the muscle forces between zero and the peak muscle force. Plausible muscle forces were constrained within six selected EMG boundaries. The spectrum to EMG-driven force difference increased from 40 to 108 N for 1-15 EMG inputs. The best-fit muscle strategy better described the EMG-driven pattern (R (2) = 0.94; RMSE = 19 N) than the static optimization solution (R (2) = 0.38; RMSE = 61 N). Possible forces for 27 of 34 muscles varied between zero and the peak muscle force, inducing a peak hip force of 11.3 body-weights. Plausible muscle forces closely matched the selected EMG patterns; no effect of the EMG constraint was observed on the remaining muscle force ranges. The model can be used to study alternative muscle recruitment strategies in both physiological and pathophysiological neuromotor conditions.
Indexed as
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.