Effective Autism Classification Through Grasping Kinematics
Bibliographic record
Abstract
Autism is a complex neurodevelopmental condition, where motor abnormalities play a central role alongside social and communication difficulties. These motor symptoms often manifest in early childhood, making them critical targets for early diagnosis and intervention. This study aimed to assess whether kinematic features from a naturalistic grasping task could accurately distinguish autistic participants from non-autistic ones. We analyzed grasping movements of autistic and non-autistic young adults, tracking two markers placed on the thumb and index finger. Using a subject-wise cross-validated classifiers, we achieved accuracy scores of above 84%. Receiver operating characteristic analysis revealed strong classification performance with area under the curve values of above 0.95 at the subject-wise analysis and above 0.85 at the trial-wise analysis. These findings indicate strong reliability in accurately distinguishing autistic participants from non-autistic ones. These findings suggest that subtle motor control differences can be effectively captured, offering a promising approach for developing accessible and reliable diagnostic tools for autism.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".