Demo Abstract: An LLM-Powered Multimodal Mobile Sensing System for Personalized and Interactive Health Behavior Analysis

Characterizing human behaviors using mobile devices is crucial for the longitudinal monitoring of chronic diseases, such as mental health conditions and Alzheimer's Disease. Current solutions often either focus on detecting basic human activities (e.g., sitting, walking) or lack comprehensive analysis and interactive guidance based on sensor data. We present MobiBox, a lightweight mobile app for long-term behavior data collection and interactive health analysis. MobiBox captures multimodal data including high-resolution 9-axis IMU data and contextual information such as APP usage and network activities, and integrates Large Language Models (LLMs) to generate personalized guidances like interventions and daily summaries. Moreover, MobiBox features a closed-loop design that allows users rate these guidances, building a high-quality dataset to enhance performance of LLMs on mobile health applications. The demo video is available at https://youtube.com/shorts/XgOXFoRaFIw?feature=share.