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Found 5 papers, 1 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.
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.
Transfer Learning for Activity Recognition in Mobile Health
While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation.
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being
In this paper, we create a benchmark for predictive analysis of individuals from a perspective that integrates: physical and physiological behavior, psychological states and traits, and job performance.
