Arm movement effect on balance
Bibliographic record
Abstract
The background research shows a high incidence of falls and loss of balance related injuries, which cause serious consequences to individual health and quality of life, as well as substantial healthcare impact in services and costs. The literature review emphasizes that arm movements have a potentially significant effect on balance, and indentifies the use of balance boards as a relevant and meaningful tool for dynamic balance evaluation. The primary objective of this initial study was to develop a method to test and evaluate the effect of arm movements on the maintenance of postural stability. Further we investigated the impact of dominant and non-dominant arms, the reaction time of arms, and the amount of activity of arms related to dynamic balance control. The study applied an accelerometer-based balance board test to measure postural stability as related to arm movements. The evaluation consists of accelerometers placed on the two arms and the balance board. Data were acquired from four different subjects and processed accordingly. The finding verified that arms play an important role in the improvement of balance. Our findings suggest that the dominant arm is more active in balance control and that the movement of arms most often occurs just prior to and during loss of balance. The results also suggest that the amount of arm movement activity directly relates to balance control and the use of the dominant arm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".