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

Chisa Tanaka, Andrew Vargo, Anna Scius-Bertrand, Andreas Fischer, Koichi Kise

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.

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

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.
Paper Structure (16 sections, 1 equation, 7 figures, 3 tables)

This paper contains 16 sections, 1 equation, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. State Estimation via Handwriting Analysis
  4. Sleep and Handwriting
  5. Proposed Method
  6. Sigma-Lognormal Model
  7. Sleep Indicators
  8. Binary Classification Framework
  9. Dataset
  10. Experimental Results and Discussion
  11. Evaluation Method
  12. Experimental Results
  13. Discussion
  14. Conclusion
  16. ...and 1 more sections

Figures (7)

  • Figure 1: Illustration of the Sigma-Lognormal model. The velocity profile (black line) is described as a sum of lognormal distributions (yellow lines).
  • Figure 2: Devices used for data collection
  • Figure 3: Visualization of writing speed for three participants (P1, P7, P13). Each row represents one participant, and each column shows data from days 1, 14, and 28 of the experiment. Color indicates stroke speed (0--300 mm/s).
  • Figure 4: Stroke order instructions for handwriting tasks
  • Figure 5: Distribution of target variables for each participant. Box plots show the median (red line), interquartile range (box), and minimum to maximum (whiskers). Blue dots represent individual daily data. Participants are ordered by ascending median of Total Sleep (P1--P13).
  • ...and 2 more figures