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.
Journal of Sleep Research18(2), 264–271 (2009)
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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.