Sigma-Lognormal handwriting features enable personalized detection of low-recovery days with PR-AUC exceeding baseline for cardiac and sleep metrics in an in-the-wild study.
Behavioral Sleep Medicine 17(2), 124–136 (2019)
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Ultra-brief student concern texts analyzed with NLP associate with lower physical activity during academic concern weeks and poorer sleep plus lower heart rate variability during emotional exhaustion weeks, complementing wearable sensing.
citing papers explorer
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From Pen Strokes to Sleep States: Detecting Low-Recovery Days Using Sigma-Lognormal Handwriting Features
Sigma-Lognormal handwriting features enable personalized detection of low-recovery days with PR-AUC exceeding baseline for cardiac and sleep metrics in an in-the-wild study.
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A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring
Ultra-brief student concern texts analyzed with NLP associate with lower physical activity during academic concern weeks and poorer sleep plus lower heart rate variability during emotional exhaustion weeks, complementing wearable sensing.