As mental health (MH) disorders become increasingly prevalent, their multifaceted
symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing …

Recent efforts to predict stress in the wild using mobile technology have increased; however,
the field lacks a common pipeline for assessing the impact of factors such as label encoding …

Mood inference with mobile sensing data has been studied in ubicomp literature over the
last decade. This inference enables context-aware and personalized user experiences in …

Understanding the dynamics of mental health among undergraduate students across the
college years is of critical importance, particularly during a global pandemic. In our study, we …

Over the years, multimodal mobile sensing has been used extensively for inferences
regarding health and well-being, behavior, and context. However, a significant challenge …

Smartphones enable understanding human behavior with activity recognition to support
people's daily lives. Prior studies focused on using inertial sensors to detect simple activities …

College students experience ever-increasing levels of stress, leading to a wide range of
health problems. In this context, monitoring and predicting students' stress levels is crucial …

Researchers in ubiquitous computing have long promised that passive sensing will
revolutionize mental health measurement by detecting individuals in a population …

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited
specialized care availability remains a major bottleneck thus hindering pre-emptive …

AI tools intend to transform mental healthcare by providing remote estimates of depression
risk using behavioral data collected by sensors embedded in smartphones. While these …