We present Trivana, an on-device mental health mobile application that enables holistic, proactive, and privacy-preserving support. The system integrates multimodal user inputs, a forecasting model for anticipating mental health risks, and a local …
Mental health challenges among college students continue to rise, motivating scalable and proactive support. Passive smartphone sensing provides an unobtrusive way to capture behavioral signals related to mental well-being, enabling machine learning …
Many users want to reduce their screen time and time spent on social media. Yet, most existing interventions rely on static rules that may not fully account for variations in user context and receptivity. In this paper, we propose the design of …
Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predicting and understanding mental health status among college students is challenging due to three main …
The COVID-19 pandemic has intensified the urgency for effective and accessible mental health interventions in people's daily lives. Mobile Health (mHealth) solutions, such as AI Chatbots and Mindfulness Apps, have gained traction as they expand …
Prefetching web pages is a well-studied solution to reduce network latency by predicting users' future actions based on their past behaviors. However, such techniques are largely unexplored on mobile platforms. Today's privacy regulations make it …