Search Results for author:
Found 5 papers, 0 papers with code
Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App
MindScape aims to study the benefits of integrating time series behavioral patterns (e. g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being.
MoodCapture: Depression Detection Using In-the-Wild Smartphone Images
MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives.
From User Surveys to Telemetry-Driven Agents: Exploring the Potential of Personalized Productivity Solutions
We present a comprehensive, user-centric approach to understand preferences in AI-based productivity agents and develop personalized solutions tailored to users' needs.
Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning
Rare life events significantly impact mental health, and their detection in behavioral studies is a crucial step towards health-based interventions.
Using Mobile Data and Deep Models to Assess Auditory Verbal Hallucinations
We then experiment with how predictive these linguistic and contextual cues from the audio diary and mobile sensing data are of an auditory verbal hallucination event.
