18 citations · 46 across the 15 of their papers we have counts for
15 papers
Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Zachary D King, Maryam Khalid, Han Yu +9
Mobile health (mHealth) systems help researchers monitor and care for patients in real-world settings. Studies utilizing mHealth applications use Ecological Momentary Assessment (E…
ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text
Han Yu, Peikun Guo, Akane Sano
The utilization of deep learning on electrocardiogram (ECG) analysis has brought the advanced accuracy and efficiency of cardiac healthcare diagnostics. By leveraging the capabilit…
AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis
Han Yu, Peikun Guo, Akane Sano
Time series data analysis is a critical component in various domains such as finance, healthcare, and meteorology. Despite the progress in deep learning for time series analysis, t…
Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
Zeyu Yang, Han Yu, Peikun Guo +3
Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis. However, current tabular diffusion models tend to inherit bias i…
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…
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…
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…
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…