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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.
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.
Personalized Step Counting Using Wearable Sensors: A Domain Adapted LSTM Network Approach
In this study, we hypothesize: (1) raw tri-axial sensor data can be modeled to create reliable and accurate step count, and (2) a generalized step count model can then be efficiently adapted to each unique gait pattern using very little new data.
