ARXIV:2603.11512 · HEALTH MONITORING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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From Pen Strokes to Sleep States: Detecting Low-Recovery Days Using Sigma-Lognormal Handwriting Features

arXiv

A framework for detecting low-recovery days through handwriting analysis and physiological metrics.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain A framework for detecting low-recovery days through handwriting analysis and physiological metrics.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

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PROBLEM

A framework for detecting low-recovery days through handwriting analysis and physiological metrics. This study examines whether daily variations in sleep-related recovery states can be inferred from online handwriting dynamics.

METHOD

Full abstract

While handwriting has traditionally been studied for character recognition and disease classification, its potential to reflect day-to-day physiological fluctuations in healthy individuals remains unexplored. This study examines whether daily variations in sleep-related recovery states can be inferred from online handwriting dynamics. % We propose a personalized binary classification framework that detects low-recovery days using features derived from the Sigma-Lognormal model, which captures the neuromotor generation process of pen strokes. In a 28-day in-the-wild study involving 13 university students, handwriting was recorded three times daily, and nocturnal cardiac indicators were measured using a wearable ring. For each participant, the lowest (or highest) quartile of four sleep-related metrics -- HRV, lowest heart rate, average heart rate, and total sleep duration -- defined the positive class. Leave-One-Day-Out cross-validation showed that PR-AUC significantly exceeded the baseline (0.25) for all four variables after FDR correction, with the strongest performance observed for cardiac-related variables. Importantly, classification performance did not differ significantly across task types or recording timings, indicating that recovery-related signals are embedded in general movement dynamics. These results demonstrate that subtle within-person autonomic recovery fluctuations can be detected from everyday handwriting, opening a new direction for non-invasive, device-independent health monitoring.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. These results demonstrate that subtle within-person autonomic recovery fluctuations can be detected from everyday handwriting, opening a new direction for non-invasive, device-independent health monitoring.

WHY NOW

Health Monitoring moved forward this cycle; last verified April 2026. Public score 5.0/10.

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Opportunity summary

Score5.0

PainA framework for detecting low-recovery days through handwriting analysis and physiological metrics.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A framework for detecting low-recovery days through handwriting analysis and physiological metrics.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competitive landscape

A framework for detecting low-recovery days through handwriting analysis and physiological metrics.

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No public code link in the paper record yet

Commercial read

5.0/10 public viability

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A framework for detecting low-recovery days through handwriting analysis and physiological metrics.

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Health Monitoring

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No public code link in the paper record yet

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5.0/10 public viability

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Adjacent

not classified

Substitute

not classified

Unknown

not classified

From Pen Strokes to Sleep States: Detecting Low-Recovery Days Using Sigma-Lognormal Handwriting Features

From Pen Strokes to Sleep States: Detecting Low-Recovery Days Using Sigma-Lognormal Handwriting Features

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