34 citations · 78 across the 17 of their papers we have counts for
8 papers · 1 filter
PiRL: Participant-Invariant Representation Learning for Healthcare
Zhaoyang Cao, Han Yu, Huiyuan Yang +1
Due to individual heterogeneity, performance gaps are observed between generic (one-size-fits-all) models and person-specific models in data-driven health applications. However, in…
Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learning
Huiyuan Yang, Han Yu, Akane Sano
Deep learning has performed remarkably well on many tasks recently. However, the superior performance of deep models relies heavily on the availability of a large number of trainin…
LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning
Han Yu, Huiyuan Yang, Akane Sano
Contrastive learning has been utilized as a promising self-supervised learning approach to extract meaningful representations from unlabeled data. The majority of these methods tak…
Bias Reducing Multitask Learning on Mental Health Prediction
Khadija Zanna, Kusha Sridhar, Han Yu +1
There has been an increase in research in developing machine learning models for mental health detection or prediction in recent years due to increased mental health issues in soci…
Exploiting Social Graph Networks for Emotion Prediction
Maryam Khalid, Akane Sano
Emotion prediction plays an essential role in mental health and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person's physiological hea…
Psychotic Relapse Prediction in Schizophrenia Patients using A Mobile Sensing-based Supervised Deep Learning Model
Bishal Lamichhane, Joanne Zhou, Akane Sano
Mobile sensing-based modeling of behavioral changes could predict an oncoming psychotic relapse in schizophrenia patients for timely interventions. Deep learning models could compl…
Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild
Han Yu, Akane Sano
Physiological and behavioral data collected from wearable or mobile sensors have been used to estimate self-reported stress levels. Since the stress annotation usually relies on se…
More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors
Huiyuan Yang, Han Yu, Kusha Sridhar +3
Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focuse…