Editorial: Current state of postural research - Moving beyond the balance platform
Bibliographic record
Abstract
Assessment of postural control with a dynamic Balance Platform was a disruptive technology at its time (1). Findings emerging from research with the dynamic platform significantly altered not only the approach to studying control of posture, but also shaped theories of postural control over subsequent decades. In this special issue, Keshner et al. argue that the most dominant paradigm shift since adoption of the posture platform was that reactive postural control is not simply a reflexive process but one that is highly dependent on the demands of the specific task as well as the state of the performer. Hall et al. review evidence for a strong linkage between emotional state and the somatic nervous system in the control of posture. Our prior special issue (2) focused on postural research that challenged the view of postural control as a reflexive mechanism. The aim of this special issue was to solicit articles that focused on the concept of posture control as a complex and adaptable motor act and were not necessarily reliant upon the dynamic balance platform as an experimental protocol. Wang et al. explored a substantial component of cortically controlled behavior known as perceptual motor style They quantified the variability that emerges both within and across individual performers and identified both static and dynamic markers that could specify the perceptual motor style. Significant heterogeneity was found across individuals performing locomotion in a height threatening virtual environment. Other markers of asymmetry in gait were explored in the lateral plane of motion, Dusane et al. showed that the ability to control lateral COM motion during walking could be a contributing factor to imbalance in people with incomplete spinal cord injury. Peterka et al. identified four major gait asymmetries (involving step width, ankle torque, stance duration and swing duration) as determinants of a separate mechanism in the control of mediolateral stability in gait. The variability of these asymmetry measures is shown to better distinguish between young and old performers than conventional measures associated with poor balance and fall risk.Results from the research presented in this issue provide further evidence to support a significant contribution of both cortical and subcortical processing, attention control networks, and sensory-motor perceptual systems to the control of posture and balance under both perturbed and unperturbed (static and dynamic) conditions. The work done initially by Nashner and colleagues has, in fact, laid the foundation to a better understanding of control of posture and gait, using newly emerging technologies (e.g., mobile imaging and wearable sensors) and commercially available equipment that mimics the dynamic platform paradigm (i.e., motorized treadmills). The adoption of these approaches to postural control research in no way negates the continued value of the dynamic platform as a crucial paradigm that could be used both in stance and gait to examine cortical control. Instead, the evidence expands the importance of the study of reactive balance control by considering the contribution of cortical networks to posture and balance.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
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".