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.
Frontiers in Psychology4, 945 (2013)
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Kinematic handwriting features from the sigma-lognormal model predict children's grade, gender, and academic performance on a large Japanese student dataset using regression and random forest models.
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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Prediction of Grade, Gender, and Academic Performance of Children and Teenagers from Handwriting Using the Sigma-Lognormal Model
Kinematic handwriting features from the sigma-lognormal model predict children's grade, gender, and academic performance on a large Japanese student dataset using regression and random forest models.