A. Sano
Latest
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Multimodal Autoencoder: A Deep Learning Approach to Filling in Missing Sensor Data and Enabling Better Mood Prediction
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Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health
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Predicting Tomorrow’s Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation
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Machine Learning of Sleep and Wake Behaviors to Classify Self-Reported Evening Mood
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Automatic identification of artifacts in Electrodermal Activity data
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Multi-task Multi-Kernel Learning for Estimating Individual Wellbeing
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Predicting students' happiness from physiology, phone, mobility, and behavioral data
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Prediction of happy-sad mood from daily behaviors and previous sleep history
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Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones
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Wavelet-based motion artifact removal for Electrodermal Activity