Natasha Jaques
Natasha Jaques
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Wearables: an R package with accompanying Shiny application for signal analysis of a wearable device targeted at clinicians and researchers
Physiological signals like heart rate and skin conductance collected from wearable devices open up a range of interesting research for clinicians and psychologists, including studying physiological reactivity to daily events and stressors. We introduce a new R package and application for analyzing wearable physiological data which enables large scale processing, and ease of use in gaining insight into this data.
P. de Looff
,
R. Duursma
,
Noordzij. Noordzi
,
S. Taylor
,
Natasha Jaques
,
F. Scheepers
,
K. De Schepper
,
S. Koldijk
2022
In
Frontiers in behavioral neuroscience
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Code
R Package
Importance of Sleep Data in Predicting Next-Day Stress, Happiness, and Health in College Students
We train personalized hierarchical Bayes models to predict individual’s next-day stress, happiness, and health, and examine the effect of including features related to sleep in the model. Including sleep features significantly improves performance when predicting happiness.
S. Taylor
,
Natasha Jaques
,
Sano, A. E. Nosakhare
,
E. B. Klerman
,
R. Picard
2017
In
Journal of Sleep and Sleep Disorders Research (suppl_1)
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Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health
Traditional, one-size-fits-all machine learning models fail to account for individual differences in predicting wellbeing outcomes like stress, mood, and health. Instead, we personalize models to the individual using multi-task learning (MTL), employing hierarchical Bayes, kernel-based and deep neural network MTL models to improve prediction accuracy by 13-23%.
Natasha Jaques
*
,
S. Taylor
*
,
E. Nosakhare
,
A. Sano
,
R. Picard
2017
In
IEEE Transactions on Affective Computing (TAFFC)
Best Paper
;
NeurIPS Machine Learning for Healthcare (ML4HC) Workshop
Best Paper
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ML4HC Best Paper
TAFFC Journal Best Paper
Understanding attention to adaptive hints in educational games: an eye-tracking study
This study uses eye tracking to assess how students interact with automatic, adaptive hints in an Intelligent Tutoring System. Specifically, we study Prime Climb, an educational game which provides individualized support for learning number factorization skills in the form of hints generated from a model of student learning.
C. Conati
,
Natasha Jaques
,
M. Muir
2013
In
International Journal of Artificial Intelligence in Education
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