Natasha Jaques
Natasha Jaques
Awards
Press
Featured
Publications
Topics
Talks
Communities
Light
Dark
Automatic
1
Prediction of happy-sad mood from daily behaviors and previous sleep history
We trained machine learning models to classify happy vs. sad moods in college students using data from surveys and wearable sensors. Factors such as poor health-related behavior, more academic activity hours, and more neutral social interactions were highly predictive of mood.
A. Sano
,
A. Z. Yu
,
A. W. McHill
,
A.J. Phillips
,
S. Taylor
,
Natasha Jaques
,
C. A. Czeisler
,
E. B. Klerman
,
R. Picard
2015
In
International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PDF
Cite
Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones
SNAPSHOT was a large-scale study of college undergraduates which tracked detailed longitudinal data from smartphones, wearable sensors, behavioral data, and mental health and sleep quality surveys. This initial study analyzed relationships between sleep quality, stress, and GPA, and used machine learning to predict these indices from objective phone and sensor data.
A. Sano
,
A. J. Phillips
,
A. Z. Yu
,
A. W. McHill
,
S. Taylor
,
Natasha Jaques
,
C. A. Czeisler
,
E. B. Klerman
,
R. Picard
2015
In
Wearable and Implantable Body Sensor Networks (BSN)
PDF
Cite
SmileTracker: Automatically and Unobtrusively Recording Smiles and their Context.
SmileTracker is an app that uses facial expression recognition to take a screenshot of the user’s screen whenever they smile. The screenshot and image of the user’s face are saved, to help them remember positive content they encountered during the day. Users can opt to share their images to a public gallery.
Natasha Jaques
,
W. V. Chen
,
R. Picard
2015
In
Proceedings of the CHI Conference Extended Abstracts
PDF
Cite
Slides
Boston Magazine article
CBC news
Wavelet-based motion artifact removal for Electrodermal Activity
We propose a method for removing motion artifacts from Electrodermal Activity using a stationary wavelet transform. We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level and skin conductance responses. Our method achieves a greater reduction of artifacts while retaining motion-artifact-free data.
W. Chen
,
Natasha Jaques
,
S. Taylor
,
A. Sano
,
S. Fedor
,
\& Picard R. Picard R
2015
In
International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PDF
Cite
Predicting Affect from Gaze Data During Interaction with an Intelligent Tutoring System
Using eye-tracking data collected while students interact with an Intelligent Tutoring System, we train machine learning models to predict when students are experiencing boredom and curiosity. Which analyze which features are most relevant to detecting when students are engaged and curious vs. disengaged and bored.
Natasha Jaques
,
C. Conati
,
J. M. Harley
,
R. Azevedo
2014
In
Intelligent Tutoring Systems
PDF
Cite
Slides
«
Cite
×