A narrative review on dynamic postural stability and neuromuscular control of balance
Bibliographic record
Abstract
Neuromuscular impairments can significantly hinder an individual’s ability to maintain balance and carry out daily activities. Understanding neuromuscular control mechanisms is critical for identifying impaired balance in the elderly and individuals with neuromuscular impairments. More specifically, being able to characterize the biomechanics of dynamic balance and the underlying neuromuscular control mechanisms is essential for identifying impairments, implementing targeted rehabilitation, and developing assistive technologies. Considering this significance, this paper reviews methodologies and findings associated with dynamic stability and the neuromuscular control mechanisms involved in dynamic postural stability. First, this review discusses two methods of quantifying dynamic stability, extrapolated center of mass and feasible stability region, and how they are used to assess stability in different postures. Next, characterizing the roles of the underlying neuromuscular control mechanisms and components involved in stabilizing human posture are discussed. The role of each component in this control system and their modeling in various postures are examined. Finally, this review discusses the potential of existing methods to characterize human dynamic stability and inform future studies. This review concludes by highlighting the need for continued research to improve our understanding of neuromuscular control mechanisms involved in postural stability and develop effective interventions for individuals with neuromuscular impairments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".