Kinematic Assessment of Fine Motor Skills in Children: Comparison of a Kinematic Approach and a Standardized Test
Bibliographic record
Abstract
Deficits in fine motor skills have been reported in some children with neurodevelopmental disorders such as amblyopia or strabismus. Therefore, monitoring the development of motor skills and any potential improvement due to therapy is an important clinical goal. The aim of this study was to test the feasibility of performing a kinematic assessment within an optometric setting using inexpensive, portable, off-the-shelf equipment. The study also assessed whether kinematic data could enhance the information provided by a routine motor function screening test (the Movement Assessment Battery for Children, MABC). Using the MABC-2, upper limb dexterity was measured in a cohort of 47 typically developing children (7-15 years old), and the Leap motion capture system was used to record hand kinematics while children performed a bead-threading task. Two children with a history of amblyopia were also tested to explore the utility of a kinematic assessment in a clinical population. For the typically developing children, visual acuity and stereoacuity were within the normal range; however, the average standardized MABC-2 scores were lower than published norms. Comparing MABC-2 and kinematic measures in the two children with amblyopia revealed that both assessments provide convergent results and revealed deficits in fine motor control. In conclusion, kinematic assessment can augment standardized tests of fine motor skills in an optometric setting and may be useful for measuring visuomotor function and monitoring treatment outcomes in children with binocular vision anomalies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".