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

2026-03-11

AI Summary

This study investigates whether everyday handwriting dynamics can reflect physiological fluctuations in healthy individuals associated with variations in sleep-related recovery, particularly for identifying low-recovery days. Leveraging the Sigma-Lognormal model to extract neuromotor features from handwriting and integrating nocturnal heart rate variability (HRV) and sleep metrics from wearable devices, the authors develop a personalized binary classification framework evaluated via Leave-One-Day-Out cross-validation. In a 28-day in-the-wild study with 13 participants, handwriting-derived features significantly outperformed the random baseline (PR-AUC = 0.25) in classifying days falling in the lowest quartile of four sleep-related metrics, with HRV-based indicators yielding the strongest performance. Notably, results were robust across different writing tasks and durations. This work establishes, for the first time, a link between handwriting dynamics and autonomic nervous system recovery, proposing a novel, non-invasive paradigm for daily health monitoring that requires no specialized equipment.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• handwriting dynamics
• sleep recovery
• physiological fluctuations
• low-recovery days
• health monitoring

Innovation

• Sigma-Lognormal model
• handwriting dynamics
• sleep recovery
• autonomic fluctuation
• personalized health monitoring