Mobile sensing systems for mental health leverage ubiquitous behavioral data to monitor states like stress and anxiety, enabling just-in-time interventions and context-aware care. However, the real-world scalability of these systems is severely limited by poor cross-user generalization—a challenge often attributed to i...
Pan-Yu Zhang, Minseo Park, Tomiris Ismatzoda et al.· Proceedings of the ACM on In...· 1 citation
Depression and anxiety are among the most prevalent mental health disorders, yet many cases remain undetected due to the lack of continuous and context-aware monitoring in everyday life. Prior work has demonstrated the potential of mobile and wearable devices for passive mental health sensing; however, their inconsiste...
Youngji Koh, Gyuna Kim, Chanhee Lee et al.· IEEE journal of biomedical a...· 0 citations
We introduce a three-wave, in-the-wild multimodal dataset for affect sensing that integrates smartphone sensing, wearable sensing, and dense experience-sampling-method (ESM) labels collected annually from 2020 to 2022. The dataset supports moment-level affect modeling through a shared dimensional label core across all...
Pan-Yu Zhang, Minseo Park, Soowon Kang et al.· 0 citations
Ethics Training Agents is proposed, a group discussion system that leverages multiple LLM participants embodying distinct ethical orientations, along with a moderator agent, to enable structured human-AI group ethical discussions for collaborative reflection.
Youngseok Seo, Sueun Jang, Hyesoo Park et al.· 0 citations
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