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REVIEW 4 major objections 5 minor 53 references

Handwriting-based Automated Assessment and Grading of Degree of Handedness: A Pilot Study

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that degree of handedness can be graded automatically from handwriting strokes, with a CNN separating dominant from non-dominant strokes at 95.06% accuracy and computational scores agreeing with the Edinburgh Inventory…

desk verdict The pilot's headline accuracy is inflated by subject leakage and its grading scale rests on an unvalidated monotonicity assumption, but the subject-disjoint ordering of accuracies makes the underlying question worth a serious referee. read the letter →

arxiv 2412.01587 v1 pith:SZNEA5AS submitted 2024-12-02 cs.AI cs.HC

Smriti Bala , Venugopalan Y. Vishnu , Deepak Joshi This is my paper · ORCID
classification cs.AIcs.HC
keywords degreeofhandednesshandwritinganalysisstrokesconvolutionalneuralnetworkEdinburghInventoryDavies-Bouldinindexlateralizationautomatedgrading
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This pilot study tries to establish that degree of handedness—how strongly someone favors one hand, as opposed to merely which hand they prefer—is measurable from handwriting. The authors segment writing into strokes using zero crossings of vertical velocity, extract kinematic and static features, and grade each subject by how well a classifier can separate their dominant-hand strokes from their non-dominant-hand strokes. A convolutional neural network operating directly on stroke coordinates and timestamps reached 95.06±3.08% average accuracy in dominant/non-dominant classification under stratified 10-fold cross-validation. Converted to a 4-point score, the CNN and MLP grades agreed with Edinburgh Inventory scores for roughly 90% of subjects within a 95% confidence interval, while giving finer resolution than the questionnaire. If right, this gives a cheap, quantitative, continuous alternative to questionnaire-based handedness assessment for rehabilitation, brain-computer interfaces, psychometry, and forensics.

What carries the argument

The load-bearing mechanism is the handwriting stroke: each writing trial is segmented between successive zero crossings of vertical velocity, so every stroke is one elementary movement unit. For the statistical and MLP pipelines, 25 time, static, and dynamic features are extracted per stroke; the DB index sums cluster-separation scores across features for each subject's dominant vs non-dominant strokes. The CNN bypasses manual features, taking padded x, y, and time channels of each stroke through three 1D convolutional layers (128, 64, 32 filters) and two fully connected layers. The grading assumption connects all of these: a high dominant-vs-nondominant classification accuracy means the two hands produce distinctly different stroke statistics, which is interpreted as a high degree of handedness, and that accuracy is linearly rescaled into a 4-point score where 4 means strongly unidextrous and 0 means fully ambidextrous.

What would settle it

Train the CNN with leave-one-subject-out on the same 43-subject dataset, then take a new group of strongly unidextrous subjects and have them write the same tasks twice: once with their non-dominant hand completely unpracticed and once after a short, controlled training session with that hand. If per-subject classification accuracy, and therefore the 4-point score, drops substantially after training while the Edinburgh Inventory score and self-reported preference are unchanged, the measure is tracking differential skill rather than an inherent degree of handedness.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that the separability of a person's dominant-hand and non-dominant-hand handwriting strokes is a valid proxy for their degree of handedness. Subjects whose strokes form two well-separated classes are "unidextrous"; subjects whose stroke distributions overlap are "ambidextrous"; intermediate separability is "partially unidextrous". The paper operationalizes this by training a CNN on raw x, y, timestamp stroke channels with leave-one-subject-out evaluation: the test subject's classification accuracy is linearly mapped to a 0–4 degree-of-handedness score. The CNN achieved 95.06±3.08% classification accuracy under stratified 10-fold cross-validation, and 90.6% of the computational scores (DB, MLP, CNN) fell within the 95% confidence interval of the Edinburgh Inventory scores after nonlinear scaling, with correlations of 0.87 (DB) and 0.91 (MLP and CNN) against scaled EI. The authors further claim this approach resolves differences the Edinburgh Inventory cannot see, since subjects with identical inventory scores received distinct computational scores.

Load-bearing premise

Everything rests on the assumption that the ease with which a classifier separates a person's dominant-hand strokes from their non-dominant-hand strokes is a faithful, monotone measure of that person's degree of handedness, rather than a reflection of practice, task familiarity, motivation, or idiosyncratic stroke style.

Editorial extensions

If this is right

  • Degree of handedness can be scored from a single short digitizer session, without expensive neuroimaging, giving a continuous quantitative measure where the Edinburgh Inventory gives only coarse semi-quantitative categories.
  • Because the CNN also labels strokes as dominant or non-dominant, a single model yields both the degree and the direction of handedness, whereas the DB-index method yields only degree.
  • Task selection matters: the loop-writing task (Task 7) separated hands best and the "llllll" task (Task 1) worst, so future versions could drop or replace low-information tasks.
  • The gender difference in scores (significant, p<<0.05) and the absence of a left/right difference suggest the measure is sensitive to lateralization-related motor organization, not merely to which hand is reported as dominant.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The 0–4 scale is obtained by linearly mapping classification accuracy to a score; the paper does not establish that the resulting scale has equal intervals, so "one point" differences may not be comparable across the range. A reader should treat the score as ordinal until a calibration study is done.
  • If the separability measure is really tracking degree of handedness, the same stroke-separability logic should transfer to other fine-motor activities such as drawing, tracing, or tapping, and could be tested as a forensic screen for feigned handedness, where subjects may control which hand they use but have trouble controlling stroke dynamics.
  • A direct testable extension: measure a subject's CNN score before and after a short block of non-dominant-hand writing practice. If the score shifts toward ambidexterity with practice while the Edinburgh score and self-reported preference stay fixed, the measure is partly a skill-asymmetry metric rather than a fixed trait.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes automated grading of degree of handedness (DoH) from digitizer-captured handwriting. Forty-three subjects were grouped as Unidextrous (U), Partially-Unidextrous (PU), or Ambidextrous (A). Stroke-level static and dynamic features were used with three approaches: a Davies-Bouldin Index score, an MLP classifier, and a CNN classifier. The CNN reaches 95.06±3.08% accuracy in stratified 10-fold cross-validation for dominant-vs-non-dominant hand classification. Leave-one-subject-out accuracies are converted through a linear mapping into a 4-point DoH score. The computed scores are compared with the Edinburgh Inventory using Bland-Altman plots, correlation, and RMSE, with roughly 90% of scores reported within the 95% confidence interval. The authors conclude that handwriting can provide a more resolved, quantitative DoH assessment than questionnaires.

Significance. If the central mapping from classification accuracy to degree of handedness were independently validated, this would be a valuable, low-cost, quantitative supplement to the Edinburgh Inventory, with plausible applications in neurorehabilitation, psychometry, and forensics. The paper includes strengths that should be acknowledged: a real data-collection protocol with ethics approval, multiple baseline methods (DB index, six classical ML classifiers, MLP, CNN), ablation studies for the MLP and CNN architectures, per-subject leave-one-subject-out results in Table II, and an explicit limitations paragraph. The pilot nature and small sample are acknowledged. However, two load-bearing issues prevent the results from currently supporting the paper's central claim: the monotone-surrogate assumption connecting per-subject classification accuracy to DoH is never independently tested, and the agreement with the Edinburgh Inventory is computed after in-sample curve fitting, which inflates agreement. The headline 95% accuracy is also based on stroke-level cross-validation with subject leakage.

major comments (4)
  1. [Section II.H Part C] The 4-point DoH score is defined as a linear rescaling of the leave-one-subject-out dominant-vs-non-dominant classification accuracy (100 mapped to 4, 0 mapped to 0). The paper states as a hypothesis that high accuracy implies unidexterity and low accuracy implies ambidexterity, but this monotone-surrogate assumption is never independently validated. High per-subject classification accuracy only shows that the model can separate that subject's dominant and non-dominant stroke kinematics; this separability may reflect practice, task familiarity, trial-order effects, or idiosyncratic stroke patterns rather than an inherent degree of handedness. The observed U>PU>A ordering is also close to definitional, because the PU group was defined by non-dominant-hand training and the A group by equal writing fluency. I request additional evidence for monotonicity, for example by correlating per-subject accuracies with a quantitative, independently measured hand-performance asymmetry that is not used to form the groups, or by showing that accuracy predicts EI scores in a held-out split with no in-sample curve fitting.
  2. [Section III.A Part B / Table II] The abstract and Section III report 'average classification accuracy of 95.06±3.08%' from stratified 10-fold cross-validation. Because the cross-validation folds are created at the stroke level, strokes from the same subject appear in both training and test folds, violating the independence assumption and inflating accuracy. The subject-level generalization results are the leave-one-subject-out accuracies in Table II, which are considerably lower and more variable across subjects. The paper should report the stroke-level CV as a within-subject discrimination result and make the leave-one-subject-out results the primary basis for the DoH-grading claims, or alternatively use a nested or grouped cross-validation that respects subject boundaries when reporting the headline accuracy.
  3. [Section I, Eqs. (2)-(6)] The comparison with the Edinburgh Inventory is compromised by in-sample curve fitting. The non-linear scaling functions for EI scores (Eqs. 2, 5, 6) are obtained by fitting curves to the same subjects whose scores are later used to compute correlations, Bland-Altman agreement, and RMSE. This procedure cannot provide an unbiased estimate of agreement; for example, a sufficiently flexible in-sample curve can make almost any two monotone sequences agree. I recommend either reporting the unscaled comparison, or using a cross-validated calibration in which scaling parameters are estimated on a training subset and evaluated on held-out subjects. Without this, the statement that 'around 90% of the obtained scores... were found to be in accordance with the EI scores under 95% confidence interval' is not supported.
  4. [Section II.B / Table II] The ambidextrous group contains only two subjects, and one of them (S22) has a condition that limits use of one hand for long periods, making the participant's handwriting data potentially atypical. The claim that the method can grade ambidexterity rests almost entirely on these two data points. I do not ask for a larger cohort in this pilot, but the conclusions should explicitly state that the ambidextrous end of the spectrum is not empirically established; the current text already mentions sample-size limitations, and I would like the Discussion and Conclusion to make this limitation more prominent when claiming that the method differentiates all three DoH categories.
minor comments (5)
  1. [Abstract / Section III.A Part B] The abstract states '95.06%' without the ±3.08% standard deviation; the full result appears in Section III, but the abstract should at least mention that this is a stroke-level stratified CV result rather than a subject-level generalization.
  2. [Section I (Introduction) / References] The introduction cites the Edinburgh Inventory [18] but does not mention Annett's questionnaire [34] in the main text until Section II.B; consider citing [34] in the introduction where questionnaire-based methods are listed.
  3. [Fig. 4] In Fig. 4, the panels {a}, {b}, and {c} are referenced in the text but the figure caption does not explain the visual elements in each panel; please expand the caption so that a reader can understand the network diagrams and the DB-index illustration without returning to the text.
  4. [Section II.F / Table I] The feature list in Table I includes 'PV' and 'Number of Strokes' but the supplementary description for 'PV' appears as 'The product of average absolute velocity and average pen pressure per segment'; please align the notation and define 'PV' in the table caption.
  5. [Section III.B] The sentence 'It is interesting to note that the EI scores' resolution were limited' contains a subject-verb agreement issue; please revise to 'the EI scores' resolution was limited'.

Circularity Check

2 steps flagged · score 6.0 of 10

The automated 'DoH score' is a linear rescaling of dominant-vs-non-dominant classification accuracy, and the EI 'accordance' is computed after an in-sample nonlinear fit; the construct-validity step is definitional rather than independently tested.

  1. self definitional [Section II.H, Part C (Grading)]
    "The subject wise classification accuracies generated by MLP and CNN were obtained. These accuracies were converted to a 4-point grading score (0-4) using the following step – The maximum accuracy for any classification could reach 100 which was mapped to 4 while minimum accuracy which could tend to reach 0 was mapped to 0, with this step the decreasing score from 4 to 0 represented increasing order of “ambidexterity” and hence lower degree of handedness and poor classification accuracy."

    The 4-point DoH score is defined as the leave-one-subject-out D-vs-ND classification accuracy divided by 25 (100 maps to 4, 0 to 0). Therefore the paper's 'test individual's DoH score' and the classifier's per-subject accuracy are the same quantity up to a linear rescale. The statement that a higher score means more unidextrous is true by construction, because the mapping was chosen under the stated hypothesis rather than derived from or tested against an independent measure of degree of handedness. Reporting this score as automated grading of DoH is thus a restatement of the classifier's hand-discriminability, not an independent measurement of DoH.

  2. fitted input called prediction [Section II.I, Eqs. (2)-(6), and Results Section III.B]
    "EI score as a function of DB score was obtained using curve fitting and was found to be non-linear ... as shown in equation (2). y = −3.1055x2 + 44.134x − 64.503 = f(x) ... Similarly, EI score as a non-linear function of 4-point score obtained from CNN using curve fitting was found in equation (6). y = 0.0950e1.6726x"

    The reported accordance with EI (90.6% within 95% CI, correlations 0.87-0.91) is computed only after fitting nonlinear EI-versus-score curves (Eqs. 2, 5, 6) on the same 43 subjects and then inverting those fits (Eqs. 3-4) to produce 'scaled EI'. Agreement is therefore measured between the computational score and a nonlinear transformation of EI that was calibrated to that same score. The high agreement is an in-sample goodness-of-fit result, not an out-of-sample prediction, so it cannot independently confirm that classification accuracy measures degree of handedness.

full rationale

The 95.06% CNN accuracy itself is not circular: the classifier was trained on dominant/non-dominant stroke labels with stratified 10-fold cross-validation, without using EI or group labels, so the core classification result stands as a hand-discrimination result. There are no load-bearing self-citations or imported uniqueness theorems. The circularity is in the construct-validity step: the 4-point 'degree of handedness' score is a linear rescaling of the per-subject D/ND accuracy, so the claim that it grades DoH is definitional under the authors' own hypothesis rather than independently established. The EI comparison, which is the only external check, is weakened because EI scores are nonlinearly curve-fitted to the computational scores on the same subjects before correlation and Bland-Altman agreement are computed. The U > PU > A ordering is also partly expected from the way the groups were defined by non-dominant-hand training and use. These issues make the central 'automated DoH grading' claim partially circular, though the underlying hand-separability result has independent content.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The paper postulates no new unobserved entities. Its empirical load is carried by fitted scaling curves (Eqs. 2, 5, 6) and the operational assumption that per-subject hand-classification accuracy equals degree of handedness.

free parameters (6)
  • Curve-fit coefficients for DB-EI scaling (Eq. 2) = -3.1055, 44.134, -64.503
    Polynomial fitted to map EI scores onto DB score range; used for Bland-Altman and correlation, so agreement is evaluated on a fitted scale.
  • Curve-fit coefficients for MLP 4-point to EI (Eq. 5) = 0.2163, 1.4724
    Exponential fit relating MLP-derived 4-point scores to EI scores on the same 43 subjects.
  • Curve-fit coefficients for CNN 4-point to EI (Eq. 6) = 0.0950, 1.6726
    Exponential fit relating CNN-derived 4-point scores to EI scores on the same 43 subjects.
  • 4-point score linear mapping = accuracy 0-100 to score 0-4
    Arbitrary linear mapping defined in Section II.H Part C; no independent calibration.
  • CNN architecture hyperparameters = 3 conv layers (128, 64, 32 filters), 2 dense layers (20 neurons), kernel 3, lr 1e-5, batch 32
    Chosen via ablation on the same dataset, not from a separate validation cohort.
  • MLP architecture hyperparameters = 2 hidden layers, 12 neurons each, ReLU
    Chosen via ablation on the same dataset.
assumptions (4)
  • domain assumption Stroke segmentation by zero crossings of vertical velocity produces behaviorally meaningful units.
    Throughout Section II.F; if segmentation is non-informative, all subsequent features and classifications inherit that flaw.
  • ad hoc to paper Per-subject dominant vs non-dominant classification accuracy is a valid, approximately monotone measure of degree of handedness.
    Stated as a hypothesis in Section II.H Part C; no independent construct validation is provided.
  • domain assumption The three groups (Unidextrous, Partially-Unidextrous, Ambidextrous) and the Edinburgh Inventory provide a valid external reference for degree of handedness.
    Group labels are self-reported and self-selected; EI is semiquantitative, as the paper itself notes.
  • domain assumption The seven selected tasks adequately sample handedness-related handwriting variation.
    Tasks were narrowed after preliminary evaluations (Section II.D), so task selection is partly informed by the same data.

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Cite this review

Pith. "Pith review of Handwriting-based Automated Assessment and Grading of Degree of Handedness: A Pilot Study." pith.science (2026). https://pith.science/paper/SZNEA5AS

@misc{pith2026241201587,
  author       = {Pith},
  title        = {Pith review of: Handwriting-based Automated Assessment and Grading of Degree of Handedness: A Pilot Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SZNEA5AS}},
  note         = {Machine review of arXiv:2412.01587}
}
read the original abstract

Hand preference and degree of handedness (DoH) are two different aspects of human behavior which are often confused to be one. DoH is a person's inherent capability of the brain; affected by nature and nurture. In this study, we used dominant and non-dominant handwriting traits to assess DoH for the first time, on 43 subjects of three categories- Unidextrous, Partially Unidextrous, and Ambidextrous. Features extracted from the segmented handwriting signals called strokes were used for DoH quantification. Davies Bouldin Index, Multilayer perceptron, and Convolutional Neural Network (CNN) were used for automated grading of DoH. The outcomes of these methods were compared with the widely used DoH assessment questionnaires from Edinburgh Inventory (EI). The CNN based automated grading outperformed other computational methods with an average classification accuracy of 95.06% under stratified 10-fold cross-validation. The leave-one-subject-out strategy on this CNN resulted in a test individual's DoH score which was converted into a 4-point score. Around 90% of the obtained scores from all the implemented computational methods were found to be in accordance with the EI scores under 95% confidence interval. Automated grading of degree of handedness using handwriting signals can provide more resolution to the Edinburgh Inventory scores. This could be used in multiple applications concerned with neuroscience, rehabilitation, physiology, psychometry, behavioral sciences, and forensics.

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Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.