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.
Abstract (translated)
虽然手写通常被研究用于字符识别和疾病分类，但它在反映健康个体日常生理波动方面的潜力尚未得到探索。本研究探讨了是否可以通过在线书写动态来推断与睡眠相关的恢复状态的日常变化。 在这项为期28天的野外研究中，13名大学学生每天记录三次手写，并通过可穿戴戒指测量夜间心脏指标。每位参与者的心率变异（HRV）、最低心率、平均心率和总睡眠时间这四个睡眠相关指标中的最低（或最高）四分位数定义了阳性类别。 在留一法交叉验证中，经过FDR校正后，所有四个变量的PR-AUC值显著高于基线（0.25），其中心脏相关的变量表现最强。值得注意的是，不同任务类型和记录时间的分类性能差异不大，表明与恢复相关的信号嵌入了一般的运动动态之中。 这些结果证明了可以通过日常书写检测到个体内部微妙的自主神经系统恢复波动，为非侵入性、设备独立性的健康监测开辟了一个新方向。
URL
https://arxiv.org/abs/2603.11512
