34 citations · 78 across the 17 of their papers we have counts for
17 papers
ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal
Han Yu, Huiyuan Yang, Akane Sano
Electrocardiogram (ECG) is an essential signal in monitoring human heart activities. Researchers have achieved promising results in leveraging ECGs in clinical applications with de…
Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data
Peikun Guo, Huiyuan Yang, Akane Sano
Data augmentation is a common practice to help generalization in the procedure of deep model training. In the context of physiological time series classification, previous research…
Towards the Understanding of Receptivity and Affect in EMAs using Physiological based Machine Learning Method: Analysis of Receptivity and Affect
Zachary D King, Han Yu, Thomas Vaessen +2
As mobile health (mHealth) studies become increasingly productive due to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior…
PiRL: Participant-Invariant Representation Learning for Healthcare Using Maximum Mean Discrepancy and Triplet Loss
Zhaoyang Cao, Han Yu, Huiyuan Yang +1
Due to individual heterogeneity, person-specific models are usually achieving better performance than generic (one-size-fits-all) models in data-driven health applications. However…
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…
Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild
Han Yu, Thomas Vaessen, Inez Myin-Germeys +1
Multimodal wearable physiological data in daily life have been used to estimate self-reported stress labels. However, missing data modalities in data collection makes it challengin…
Patient-independent Schizophrenia Relapse Prediction Using Mobile Sensor based Daily Behavioral Rhythm Changes
Bishal Lamichhane, Dror Ben-Zeev, Andrew Campbell +9
A schizophrenia relapse has severe consequences for a patient's health, work, and sometimes even life safety. If an oncoming relapse can be predicted on time, for example by detect…
Forecasting Health and Wellbeing for Shift Workers Using Job-role Based Deep Neural Network
Han Yu, Asami Itoh, Ryota Sakamoto +2
Shift workers who are essential contributors to our society, face high risks of poor health and wellbeing. To help with their problems, we collected and analyzed physiological and…
Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients
Joanne Zhou, Bishal Lamichhane, Dror Ben-Zeev +2
We aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data towards relapse prediction tasks. The identified clusters co…
Sensor-Based Estimation of Dim Light Melatonin Onset (DLMO) Using Features of Two Time Scales
Cheng Wan, Andrew W. McHill, Elizabeth Klerman +1
Circadian rhythms influence multiple essential biological activities including sleep, performance, and mood. The dim light melatonin onset (DLMO) is the gold standard for measuring…