Smartphone-based Human Behavior Task Modeling for Explainable Mental Health Detection Model

Smartphone data-driven mental health detection approach is widely used. However, existing methods focus primarily on enhancing model performance through numerous superficial smartphone usage features that do not effectively capture human behaviors, rather than interpreting the model's explanatory power. This study proposes a methodology of smartphone-based human behavior task modeling to advance "Explainable AI (X-AI)" for detecting mental health states. Our approach utilizes in-app UI-level trajectory data to extract more fine-grained human behavior task mining and modeling. We introduced novel concepts to investigate the nature and scope of smartphone-based human behavior tasks. This study discusses how these concepts can enhance the trustworthiness and transparency of models for both users and system providers. Furthermore, we expect that our approach could reduce data collection, thereby reducing memory usage and mitigating privacy concerns by enabling on-device learning.