Evolution of postural control assessment: From dynamic posturography to virtual reality
Bibliographic record
Abstract
During the early years of spaceflight it was documented that astronauts were impaired and incapacitated upon return to earth. Computerized Dynamic Posturography (CDP) was devised to investigate and quantify this deficit, and eventually progressed into a clinical assessment tool. The current sprouting of virtual reality (VR) technologies has allowed for the development of an alternative approach that could be more informative. Many low-cost VR systems (including desktop gaming programs designed for rehabilitation) are now available. Continued improvements in this technology indicate a high probability that VR will become an integral component of posturography by replacing present mechanical CDP techniques. We researched the relevant literature to evaluate the strengths and weaknesses of CDP using the Equitest (Neurocom International; Clackamas USA), and the added benefits of incorporating VR to help clinicians assess the complex task of balance maintenance. VR is capable of manipulating task and environmental demands in order to assess functional postural behavior. VR is also a useful tool for clinical testing of postural disorders resulting from sensory mismatch. Although posturography is still a useful clinical tool, VR provides an inherent conflict between the visual and vestibular senses and can elevate the effectiveness of CDP for both assessment and intervention. We conclude that, when initially developed, CDP was innovative and ahead of its time. However, with the advent of VR, we have a chance to modernize CDP and enhance its value as a clinical instrument.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".