Interactive Video Game-Based Tool for Dynamic Rehabilitation Movements
Bibliographic record
Abstract
In being seated, standing, and walking, many uncontrollable factors contribute to the degradation of our balance system. The maintenance of balance involves many essential sensory (visual, vestibular, and somatosensory) and motor processes. Each sensory input provides unique internal and external reference frame information to the central nervous system (CNS). The CNS interprets the sensory information, from which preplanned and/or preventative (feedforward controls) and corrective (feedback controls) actions can be taken and conflicting sensory information can be mediated (Peterka, 2002). In the absence of a sensory input, balance can still be maintained; however, the compensatory actions become larger and different balance strategies may be employed. Serious problems facing older adults and many people with neurological disorders (e.g., stroke, traumatic head injuries, incomplete spinal cord injuries, Parkinson’s, multiple sclerosis, diabetic peripheral neuropathy, and osteoarthritis) are balance impairment, mobility restriction, and falling (Gill et al., 2001; Harris, Eng, Marigold, Tokuno, & Louis, 2005). In these cases, even small disturbances may result in a fall and injuries are very likely to occur. This increased risk of falling combined with mobility limitations precipitates patient dependency in instrumental and basic activities of daily living; in turn, this results in reduced levels of physical activity.Request access from your librarian to read this chapter's full text.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.109 | 0.034 |
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".