Healthcare

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)
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Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones
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)
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Wavelet-based motion artifact removal for Electrodermal Activity