The 2022 Conference on Movement and Cognition
Bibliographic record
Abstract
Objectives: 1) Explore the connections between posture and cognition, 2) examine the measurable changes in posture and motor skills observed with the use of frequency resonating insoles in older adults, as seen in pilot study collaboration with McGill University, 3) discuss relevant research findings of posture in cognitive performance and 4) review potential areas for future study involving these factors.Methods: Research has been conducted using fMRI to test cognitive functions in supine and upright postures, as well as with dual tasking requiring performance of a postural and cognitive task.In clinic, participants were fitted with 90 Hz frequency postural insoles that they were required to wear in closed shoes during all waking hours, for two weeks.Results: A study by Meuhlhan et al has shown a difference in cognitive ability in an upright posture verses a supine posture.Outcomes of in clinic studies included: two of eight participants reporting an improvement in their balance due to full time wearing frequency insoles while the remaining participants reported no change, and measurements for the surface area and distance travelled by the COG in the force plate analyses showed significant improvements for the eyes closed conditions, while the other conditions were insignificant.Conclusions: It has been recommended that the elderly reinforce cognition to improve balance and gait, but will the inverse work as well?The study performed in collaboration with McGill University was the first study to examine the short-term effect of postural insoles on the objective and subjective experiences of postural stability for healthy older adults.Recent research has shown a link between posture and cognitive ability.Further studies of the effectiveness of frequency resonating insoles in improving postural stability, and the impact this change in posture might have on cognition and neurodegenerative conditions would be beneficial.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.301 | 0.202 |
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".