Evaluating eye-hand coordination with digital technologies
Bibliographic record
Abstract
Digital tools, such as computerized wobble boards (WB), offer a novel approach to assess dynamic balance in lower limbs. Since their potential for evaluating eye-hand coordination remains unexplored, this study assessed the inter-test reliability and concurrent validity of WB measurements for upper limb fine motor skills, proposing WB as an alternative to the Grooved Pegboard test (GPT). Fifty-three healthy participants completed WB and GPT tests, and a WB retest after 48 h. The custom WB software displayed real-time performance via a motion marker and target zone. Participants moved the marker within the target zone following predefined patterns (clockwise, counterclockwise, anteroposterior, mediolateral) across four 15-s trials per hand. Performance was quantified as the duration (s) the marker remained in the target zone under each condition. According to Intraclass correlation coefficients (ICC) WB demonstrated good to excellent reliability (ICC: 0.62–0.80), acceptable Standard Error of Measurement ( SEM : 0.96–2.14 s), and minimal detectable change (MDC 95 : 1.90–4.25 s). Moderate to strong correlations (r = − 0.30 to − 0.54) between WB and GPT outcomes suggested WB captured related aspects of fine motor coordination. These findings confirm WB’s reliability and validity as a tool for assessing eye-hand coordination. Further validation is needed in training or rehabilitation contexts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".